{"query": "Please generate the Abstract section for this paper.", "gt": "Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) have been observed to assign lower uncertainty to the OOD than the target distribution. In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images. Based on the observation, we construct a novel RND-based OOD detector, SVD-RND, that utilizes blurred images during training. Our detector is simple, efficient at test time, and outperforms baseline OOD detectors in various domains. Further results show that SVD-RND learns better target distribution representation than the baseline RND algorithm. Finally, SVD-RND combined with geometric transform achieves near-perfect detection accuracy on the CelebA dataset.", "turbo-16k-0613_pred": "In this paper, the authors propose a new method for out-of-distribution (OOD) detection using blurred data as adversarial examples. They introduce a model called SVD-RND, which combines the idea of random network distillation (RND) with the concept of singular value decomposition (SVD). The SVD-RND model is trained to discriminate between original and blurred datasets by using the l2 loss against target networks. The authors evaluate the performance of SVD-RND on challenging scenarios and compare it to conventional baselines. The results show that SVD-RND outperforms other methods in detecting OOD data, achieving over 90% accuracy in several domains. Additionally, SVD-RND shows improvements even in domains where conventional OOD detection schemes only achieve moderate results. Overall, the use of blurred data and the SVD-RND model provide a significant performance gain in OOD detection.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Generative adversarial nets , Authors: Ian J. Goodfellow Jean Pouget-Abadie... , 2014\n2.  Title: Explaining and harnessing adversarial examples , Authors: Ian J. Goodfellow Jonathon Shlens... , 2014\n3.  Title: Do deep generative models know what they don\u2019t know , Authors: Eric Nalisnick Akihiro Matsukawa... , 2019\n4.  Title: Detecting out-of-distribution inputs to deep generative models using a test for typicality , Authors: Eric Nalisnick Akihiro Matsukawa... , 2019\n5.  Title: Generative probabilistic novelty detection with adversarial autoencoders , Authors: Stanislav Pidhorskyi Ranya Almohsen... , 2018\n6.  Title: Deep one-class classification , Authors: Lukas Ruff Robert Vandermeulen... , 2018\n7.  Title: Deep semi-supervised anomaly detection , Authors: Lukas Ruff Robert Vandermeulen... , 1906\n8.  Title: Adversarially learned one-class classifier for novelty detection , Authors: Mohammad Sabokrou Mohammad Khalooei... , 2018\n9.  Title: Efficient GAN-based anomaly detection , Authors: Houssam Zenati Chuan Sheng Foo... , 2018\n", "gt": "The goal of OOD detection is to determine whether the data is sampled from the target distribution D. Therefore, based on the training data Dtrain \u0102 D, we train a scalar function that expresses the\nconfidence, or uncertainty of the data. The performance of the OOD detector is tested on the Dtest \u0102 D against the OOD dataset DOOD. We denote an in-distribution data and OOD pair as In : Out in this paper, e.g., CIFAR-10 : SVHN. In this section, we mention only closely related work to our research. For a broader survey on deep OOD detection, we recommend the paper from Chalapathy et al. (2019). OOD Detection: Majority of OOD detection methods rely on a reconstruction error and representation learning. (Ruff et al., 2018) trained a deep neural network to map data into a minimum volume hypersphere. Generative probabilistic novelty detection (GPND) (Pidhorskyi et al., 2018) employed the distance to the latent data manifold as the confidence measure and trained the adversarial autoencoder (AAE) to model the manifold. Deep generative models are widely employed for latent space modeling in OOD detection (Zenati et al., 2018; Sabokrou et al., 2018). However, a recent paper by Nalisnick et al. (2019) discovered that popular deep generative models, such as variational autoencoder (VAE) (Kingma et al., 2014) or GLOW (Kingma et al., 2018), fail to detect simple OOD. While adversarially trained generative models, such as generative adversarial networks (GAN) (Goodfellow et al., 2014) or AAE, are not discussed in Nalisnick et al. (2019), our experiments with GPND show that such models can also struggle to detect such simple OODs. OOD Detection with Additional Data: Some methods try to solve OOD detection by appending additional data or labels for training.", "turbo-16k-0613_pred": "In recent years, there has been significant interest in out-of-distribution (OOD) or novelty detection, which aims to distinguish samples that come from an unseen distribution from those in the training distribution. Current methods in this field focus on noise filtering or representation learning, such as using autoencoders to detect OOD data. However, deep generative models have also been widely used for OOD detection despite concerns about their effectiveness. Adversarial examples have been employed to fool classifiers, and training classifiers against adversarial attacks has been shown to be effective in detecting unknown adversarial attacks. In this paper, the authors propose using blurred data as adversarial examples and apply Singular Value Decomposition (SVD) to generate the blurred images. They train a new OOD detection model called SVD-RND using the idea of Random Network Distillation (RND) to discriminate between the original data and the blurred versions. The proposed SVD-RND model is evaluated in challenging scenarios, and it outperforms conventional baselines, achieving significant performance gains in various domains. The authors also compare the performance of SVD-RND with other blurring techniques, such as Discrete Cosine Transform (DCT) and Gaussian Blurring, and show that SVD provides better results. Overall, the results demonstrate the effectiveness of SVD-RND in OOD detection and suggest its potential for practical applications in various domains. \n\nThe Related Work section for this paper can be generated as follows:\n\nRelated Work\nNovelty detection and OOD detection have been extensively studied in recent years. One approach is to use deep generative models for OOD detection. Goodfellow et al. (2014) introduced generative adversarial nets (GANs), which have shown promising results in various tasks, including novelty detection. Nalisnick et al. (2019) and Choi et al. (2018) raised concerns about the OOD detection performance of deep generative models, as they tend to assign higher likelihood to OOD data than training data. Pidhorskyi et al. (2018) proposed generative probabilistic novelty detection with adversarial autoencoders, which models uncertainty as a deviation from the latent representation. Ruff et al. (2018) introduced deep one-class classification and deep semi-supervised anomaly detection, which aim to learn representations of the in-distribution data for better OOD detection. Sabokrou et al. (2018) and Zenati et al. (2018) focused on adversarial-based approaches for novelty detection. These methods have shown promising results, but there is still room for improvement.\n\nIn this paper, the authors propose a new OOD detection model called SVD-RND. They use blurred data as adversarial examples and apply Singular Value Decomposition (SVD) to generate the blurred images. SVD-RND is trained using the idea of Random Network Distillation (RND) (Burda et al., 2019) to discriminate between the original data and the blurred versions. The results of their experiments demonstrate that SVD-RND outperforms conventional baselines, such as GPND (Pidhorskyi et al., 2018) and RND (Burda et al., 2019), achieving significant performance gains in various domains. Furthermore, the authors compare the performance of SVD-RND with other blurring techniques, such as Discrete Cosine Transform (DCT) and Gaussian Blurring, and show that SVD provides better results. Overall, the proposed SVD-RND model offers a promising approach for OOD detection and contributes to the existing literature on deep generative models and novelty detection.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "Summary: They tackle the problem of out-of-data distribution by leveraging RND applied to data augmentations. They train a model f(x) to match the outputs of g_i(aug_i(x)), where g_i is a random network and aug_i is a particular type of augmentation. An example with high error in this task is treated as an out-of-distribution example. This work focuses on exploring blurring through SVD, where the smallest K singular values are set to 0, and K varies between different aug_i calls. They find that their method of consistently can achieve strong detection rates across multiple target-dataset pairs.\n\nComments:\n* The experimental results in this work are impressive, which introduces many more questions.\n* The model used for f and g is not mentioned in the text.\n* Figure 4 (left) suggests that the SVD-RND performs about the same between 10K and 50K examples. The level of robustness is surprising, but doesn\u2019t seem to square with intuition that more data ought to help. How little data can be used? In other words, extend the graph to the left. \n* The geometric transforms baseline is not fair, since SVD-RND uses multiple SVD transforms (b_train > 1) whereas the geometric transforms only have one. Please run a model with all the geometric transforms. This result is important for understanding whether the gains come from the particular transform (SVD) or the number of transforms used.\n* Following the spirit of the previous comment, what other data augmentations can be used in place of SVD? Typical image classification pipelines use a large variety of augmentations. I would suggest taking some augmentations from AutoAugment [1] and running RND on top of them.\n* An experiment that is missing is RND trained on blurred images. Is the blurring itself the important component, or is having multiple different heads important?\n* In general, I am confused about how a single head RND does not converge to 0 loss by learning the weights of g. This seems to be a simple optimization problem. The original RND paper avoided this problem by also using the network to learn a policy, but this does not exist in this approach.\n\nIn general, this work seems promising, but lacks proper ablations that elucidate what components of the method are important. I am happy to increase my score if the experiments suggests are added to the work.\n\n6: Weak Accept", "turbo-16k-0613_pred": "Review:\nThe paper proposes a new OOD (out-of-distribution) detection model called SVD-RND, which utilizes blurring techniques to prevent overfitting to low-resolution data. The authors demonstrate that deep generative models, while widely used for novelty detection, tend to assign higher likelihood to OOD data than training data. Motivated by this, the authors employ blurring techniques and propose SVD-RND, which trains a predictor network to discriminate between original and blurred datasets.\n\nThe experimental results show that SVD-RND achieves significant performance gains compared to conventional baselines, particularly in challenging scenarios such as CIFAR-10 vs. SVHN and ImageNet vs. CIFAR-10. Furthermore, SVD-RND shows improvements over baselines in domains where conventional OOD detection schemes only achieve moderate results.\n\nThe reviewers have provided valuable feedback on their experiments, including comparisons with other blurring techniques and additional analyses. The paper demonstrates that SVD-RND outperforms other baselines in various In : Out domains and shows superior target distribution representation compared to the baseline RND model.\n\nOverall, this paper presents an effective approach to OOD detection using blurring techniques and demonstrates significant performance gains over conventional methods. The experiments are thorough and well-designed, and the results are convincing. However, further investigations could include analyzing the impact of different blurring parameters and exploring the scalability of SVD-RND to larger datasets.\n\nSuggestions:\n1. Investigate the impact of different blurring parameters, such as the number of discarded singular values, on the performance of SVD-RND. This analysis could provide further insights into the behavior of the model and help optimize the blurring technique.\n\n2. Explore the scalability of SVD-RND to larger datasets, such as ImageNet, to evaluate its performance in more complex and diverse OOD scenarios. This would provide a better understanding of the model's generalization capabilities and potential real-world applications.\n\nQuestions:\n1. Could SVD-RND be applied to other domains or tasks beyond image classification? For example, could it be used for text classification or audio analysis?\n\n2. How does SVD-RND compare to other state-of-the-art OOD detection methods, such as those based on anomaly detection algorithms or active learning approaches? Is there any potential for combining SVD-RND with these methods to further improve performance?\n\n3. Are there any limitations or potential drawbacks of using blurring techniques for OOD detection? Could blurring distort the original data too much, leading to potential misclassifications or false positives in certain scenarios?", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.\u2020", "turbo-16k-0613_pred": "The paper introduces a novel architecture for self-supervised monocular depth estimation that leverages semantic information to improve depth prediction. The proposed architecture uses a fixed pre-trained semantic segmentation network to guide geometric representation learning in a self-supervised monocular depth network. The architecture uses pixel-adaptive convolutions to learn semantic-dependent representations that capture equivariance between the visual appearance of objects and their depth. The paper also introduces a two-stage training process to address the bias on dynamic objects caused by the infinite depth problem. The results show that the proposed architecture outperforms the state-of-the-art methods in self-supervised monocular depth estimation on the KITTI benchmark. An ablative analysis is conducted to evaluate the generalization of the proposed architecture to different depth networks, and class-specific evaluation metrics demonstrate consistent improvements for all predicted classes. The paper concludes by stating that the proposed framework establishes a new state of the art in self-supervised monocular depth estimation and suggests future research directions, such as leveraging other sources of guidance and self-supervised fine-tuning of the semantic network.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Exploiting semantic information and deep matching for optical flow , Authors: Min Bai Wenjie Luo... , 2016\n2.  Title: Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos , Authors: Vincent Casser Soeren Pirk... , 2019\n3.  Title: Depth map prediction from a single image using a multi-scale deep network , Authors: David Eigen Christian Puhrsch... , 2014\n4.  Title: Deep ordinal regression network for monocular depth estimation , Authors: Huan Fu Mingming Gong... , 2018\n5.  Title: Unsupervised cnn for single view depth estimation: Geometry to the rescue , Authors: Ravi Garg Vijay Kumar BG... , 2016\n6.  Title: Unsupervised monocular depth estimation with left-right consistency , Authors: Cl\u00e9ment Godard Oisin Mac Aodha... , 2017\n7.  Title: Digging into selfsupervised monocular depth prediction , Authors: Cl\u00e9ment Godard Oisin Mac Aodha... , 2018\n8.  Title: Packnet-sfm: 3d packing for self-supervised monocular depth estimation , Authors: Vitor Guizilini Sudeep Pillai... , 2019\n9.  Title: Spatial transformer networks. In Advances in neural information processing , Authors: Max Jaderberg Karen Simonyan... , 2017\n10.  Title: Single-image depth estimation based on fourier domain analysis , Authors: Jae-Han Lee Minhyeok Heo... , 2018\n11.  Title: Spigan: Privileged adversarial learning from simulation , Authors: Kuan-Hui Lee German Ros... , 2019\n12.  Title: Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs , Authors: Bo Li Chunhua Shen... , 2015\n13.  Title: Learning to fuse things and stuff , Authors: Jie Li Allan Raventos... , 2018\n14.  Title: Unsupervised learning of depth and egomotion from monocular video using 3d geometric constraints , Authors: Reza Mahjourian Martin Wicke... , 2018\n15.  Title: Geonet: Geometric neural network for joint depth and surface normal estimation , Authors: Xiaojuan Qi Renjie Liao... , 2018\n16.  Title: Demon: Depth and motion network for learning monocular stereo , Authors: Benjamin Ummenhofer Huizhong Zhou... , 2017\n17.  Title: Learning depth from monocular videos using direct methods , Authors: Chaoyang Wang Jos\u00e9 Miguel Buenaposada... , 2018\n18.  Title: Image quality assessment: from error visibility to structural similarity , Authors: Zhou Wang Alan C Bovik... , 2004\n19.  Title: Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry , Authors: Nan Yang Rui Wang... , 2018\n20.  Title: Unsupervised learning of geometry with edge-aware depth-normal consistency , Authors: Zhenheng Yang Peng Wang... , 2017\n", "gt": "Since the seminal work of Eigen et al. (2014), substantial progress has been done to improve the accuracy of supervised depth estimation from monocular images, including the use of Conditional Random Fields (CRFs) (Li et al., 2015), joint optimization of surface normals (Qi et al., 2018), fusion of multiple depth maps (Lee et al., 2018), and ordinal classification (Fu et al., 2018). Consequently, as supervised techniques for depth estimation advanced rapidly, the availability of largescale depth labels became a bottleneck, especially for outdoor applications. Garg et al. (2016) and Godard et al. (2017) provided an alternative self-supervised strategy involving stereo cameras, where Spatial Transformer Networks (Jaderberg et al., 2015) can be used to geometrically warp, in a differentiable way, the right image into a synthesized left image, using the predicted depth from the left image. The photometric consistency loss between the resulting synthesized and original left images can then be minimized in an end-to-end manner using a Structural Similarity term (Wang et al., 2004) and additional depth regularization terms. Following Godard et al. (2017) and Ummenhofer et al. (2017), Zhou et al. (2017) generalized this to the purely monocular setting, where a depth and a pose networks are simultaneously learned from unlabeled monocular videos. Rapid progress in terms of architectures and objective functions (Yin & Shi, 2018; Mahjourian et al., 2018; Casser et al., 2019; Zou et al., 2018; Klodt & Vedaldi, 2018; Wang et al., 2018; Yang et al., 2018) have since then turned monocular depth estimation into one of the most successful applications of self-supervised learning, even outperforming supervised methods (Guizilini et al., 2019). The introduction of semantic information to improve depth estimates has been explored in prior works, and can be broadly divided into two categories. The first one uses semantic (or instance) information to mask out or properly model dynamic portions of the image, which are not accounted for in the photometric loss calculation. Gu\u0308ney & Geiger (2015) leveraged object knowledge in a Markov Random Field (MRF) to resolve stereo ambiguities, while Bai et al.", "turbo-16k-0613_pred": "The task of depth estimation in computer vision and robotics is crucial for perception, navigation, and planning. While dedicated sensors are typically used for depth perception, learning to predict depth from monocular imagery has the potential to provide useful cues for various tasks. Self-supervised methods, which exploit geometry as supervision, have shown promise in leveraging large-scale datasets of raw videos to outperform supervised methods. In this paper, the authors propose a novel architecture that uses a fixed pre-trained semantic segmentation network to guide geometric representation learning in a self-supervised monocular depth network. The architecture incorporates pixel-adaptive convolutions to capture semantic-dependent representations and introduces a two-stage training process to remove biases introduced by semantics. Experimental results on the KITTI benchmark demonstrate that the proposed architecture outperforms state-of-the-art self-supervised monocular depth estimation methods, both on average over pixels and for dynamic categories in particular. The authors also provide an ablative analysis and evaluate the generalization capability of their approach on the NuScenes dataset, further confirming the effectiveness of their method. The source code and pretrained models are available on GitHub. \n\nIn related work, Bai et al. (2016) use semantic information and deep matching for optical flow estimation. Casser et al. (2019) propose a depth prediction method from monocular videos without the use of sensors. Eigen et al. (2014) present a multi-scale deep network for depth map prediction from a single image. Fu et al. (2018) propose a deep ordinal regression network for monocular depth estimation. Garg et al. (2016) propose unsupervised CNN for single view depth estimation using geometry. Godard et al. (2017) and Godard et al. (2018) introduce unsupervised monocular depth estimation with left-right consistency and digging into self-supervised monocular depth prediction, respectively. Guizilini et al. (2019) present PackNet-SfM, a self-supervised monocular depth estimation method. Jaderberg et al. (2017) propose spatial transformer networks. Lee et al. (2018) present single-image depth estimation based on Fourier domain analysis. Lee et al. (2019) propose Spigan, which leverages privileged adversarial learning from simulation. Li et al. (2015) present depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs. Li et al. (2018) propose learning to fuse things and stuff. Mahjourian et al. (2018) present unsupervised learning of depth and egomotion from monocular video using 3D geometric constraints. Qi et al. (2018) propose GeoNet, a geometric neural network for joint depth and surface normal estimation. Ummenhofer et al. (2017) propose Demon, a depth and motion network for learning monocular stereo. Wang et al. (2018) present learning depth from monocular videos using direct methods. Wang et al. (2004) propose a structural similarity-based image quality assessment. Yang et al. (2018) propose deep virtual stereo odometry. Yang et al. (2017) introduce unsupervised learning of geometry with edge-aware depth-normal consistency. These papers provide a comprehensive background and related work on depth estimation and self-supervised learning.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes a using pixel-adaptive convolutions to leverage semantic labels in self-supervised monocular depth estimation. The semantic features are predicted by a pretrained network rather than relying on a ground truth. Moreover, a two-stage training process in proposed in order to filter out images leading to erroneous SfM predictions. The method is evaluated with different networks on the KITTY dataset.\n\nThe paper is very well written and clear. The applications of per-pixel convolutions to this problem seems sound and the experimental validation seems satisfactory. I have however one main concern (1) and a few additional questions below:\n\n1) While (Guizilini 2019) shows that using a larger set of unannotated videos and allows the self-supervised method to eventually outperform supervised methods, this study is not done here. This makes me question the applicability of the approach, as using large unlabelled videos would probably lead to noisy segmentations that could be unhelpful to the depth estimation. Showing an improvement over the supervised baseline would be a much stronger experimental validation, as for now it is difficult to know exactly why in which scenario this method should be used, rather than a supervised network or vanilla packnet.\n\n2) I see that you obtain the same numbers in Table 2 / PackNet / row 1 as in (Guizilini 2019); I would like to confirm that you used exactly their self-objective loss, in all your experiments? I would suggest adding to section 3.1. the fact the fact that the loss is the same is in (Guizlini 2019), as a reader could assume that there is novelty in the loss formulation.\n\n3) Have you tried fine-tuning the whole architecture including the semantic network end-to-end?\n\n6: Weak Accept", "turbo-16k-0613_pred": "Title: Review of \"Semantically-Guided Depth Estimation in a Self-Supervised Monocular Depth Network\"\n\nReview:\nThe paper \"Semantically-Guided Depth Estimation in a Self-Supervised Monocular Depth Network\" addresses the problem of depth estimation in computer vision and robotics. The authors propose a novel architecture that leverages semantic information to improve monocular depth prediction in a self-supervised way. They introduce pixel-adaptive convolutions and a two-stage training process to guide geometric representation learning and overcome common biases. The experimental results on the KITTI benchmark demonstrate that their approach outperforms state-of-the-art methods in terms of average and class-specific depth prediction accuracy.\n\nThe paper presents a comprehensive analysis of their proposed architecture and provides compelling evidence of its effectiveness. The main contribution of leveraging semantic information to improve depth estimation is supported by quantitative metrics and qualitative results. The authors also explore the generalizability of their method by evaluating it on the NuScenes dataset, achieving better performance compared to other methods.\n\nWhile the paper is well-written and the methodology is sound, there are still some areas that could be further addressed. Firstly, the authors could delve into the limitations and potential drawbacks of their approach, such as the requirement of a semantic segmentation network and its impact on overall model complexity. Additionally, more discussion on the scalability and potential applications of their method beyond the specific datasets used in the experiments would be valuable.\n\nFurthermore, the authors could provide insights into the transferability of their semantically-guided architecture to tasks other than depth estimation, as well as the potential for combining it with other sources of guidance, such as optical flow or surface normals. Lastly, it would be interesting to investigate the robustness of their method to scenarios where the semantic network's predictions are unreliable or uncertain, and its impact on depth quality in those cases.\n\nOverall, the paper presents a novel approach to leveraging semantic information for self-supervised monocular depth estimation. The experimental results are impressive, and the proposed architecture shows promise for advancing the state of the art in depth estimation. Further exploration of the limitations, generalizability, and potential use cases would add depth to the research presented in the paper.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We analyze speed of convergence to global optimum for gradient descent training a deep linear neural network (parameterized as x 7\u2192 WNWN\u22121 \u00b7 \u00b7 \u00b7W1x) by minimizing the `2 loss over whitened data. Convergence at a linear rate is guaranteed when the following hold: (i) dimensions of hidden layers are at least the minimum of the input and output dimensions; (ii) weight matrices at initialization are approximately balanced; and (iii) the initial loss is smaller than the loss of any rank-deficient solution. The assumptions on initialization (conditions (ii) and (iii)) are necessary, in the sense that violating any one of them may lead to convergence failure. Moreover, in the important case of output dimension 1, i.e. scalar regression, they are met, and thus convergence to global optimum holds, with constant probability under a random initialization scheme. Our results significantly extend previous analyses, e.g., of deep linear residual networks (Bartlett et al., 2018).", "turbo-16k-0613_pred": "This paper presents a convergence analysis of gradient descent for deep linear neural networks. It focuses on the properties of approximate balancedness and deficiency margin at the initialization step, showing that violating either of these assumptions can lead to convergence failure. The authors prove that under the conditions of approximate balancedness and deficiency margin, gradient descent converges to the global minimum at a linear rate. The paper also discusses the importance of balanced initialization and provides empirical evidence that supports the theoretical findings. The experiments show that balanced initialization leads to faster and more stable convergence compared to the customary layer-wise independent initialization. The authors conclude by suggesting that similar ideas can be applied to the analysis of gradient descent for non-linear neural networks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Neural networks and principal component analysis: Learning from examples without local minima , Authors: Pierre Baldi Kurt Hornik , 1989\n2.  Title: The loss surfaces of multilayer networks , Authors: Anna Choromanska Mikael Henaff... , 2015\n3.  Title: On the power of over-parametrization in neural networks with quadratic activation , Authors: Simon S Du Jason D Lee , 2018\n4.  Title: Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced , Authors: Simon S Du Wei Hu... , 2018\n5.  Title: When is a convolutional filter easy to learn , Authors: Simon S Du Jason D Lee... , 2018\n6.  Title: Gradient descent learns onehidden-layer cnn: Dont be afraid of spurious local minima , Authors: Simon S Du Jason D Lee... , 2018\n7.  Title: Escaping from saddle pointsonline stochastic gradient for tensor decomposition , Authors: Rong Ge Furong Huang... , 2015\n8.  Title: Matrix completion has no spurious local minimum , Authors: Rong Ge Jason D Lee... , 2016\n9.  Title: Global optimality in neural network training , Authors: Benjamin Haeffele Ren\u00e9 Vidal , 2017\n10.  Title: Identity matters in deep learning , Authors: Moritz Hardt Tengyu Ma , 2016\n11.  Title: Deep learning without poor local minima , Authors: Kenji Kawaguchi , 2016\n12.  Title: Deep linear networks with arbitrary loss: All local minima are global , Authors: Thomas Laurent James Brecht , 2018\n13.  Title: Gradient descent only converges to minimizers , Authors: Jason D Lee Max Simchowitz... , 2016\n14.  Title: The loss surface of deep and wide neural networks , Authors: Quynh Nguyen Matthias Hein , 2017\n15.  Title: The loss surface and expressivity of deep convolutional neural networks , Authors: Quynh Nguyen Matthias Hein , 2018\n16.  Title: Gradient descent only converges to minimizers: Non-isolated critical points and invariant regions , Authors: Ioannis Panageas Georgios Piliouras , 2017\n17.  Title: Spurious local minima are common in two-layer relu neural networks , Authors: Itay Safran Ohad Shamir , 2018\n18.  Title: Exponential convergence time of gradient descent for one-dimensional deep linear neural networks , Authors: Ohad Shamir , 2018\n19.  Title: No bad local minima: Data independent training error guarantees for multilayer neural networks , Authors: Daniel Soudry Yair Carmon , 2016\n", "gt": "Theoretical study of gradient-based optimization in deep learning is a highly active area of research. As discussed in Section 1, a popular approach is to show that the objective landscape admits the properties of no poor local minima and strict saddle, which, by Ge et al. (2015); Lee et al. (2016); Panageas and Piliouras (2017), ensure convergence to global minimum. Many works, both classic (e.g. Baldi and Hornik (1989)) and recent (e.g. Choromanska et al. (2015); Kawaguchi (2016); Hardt and Ma (2016); Soudry and Carmon (2016); Haeffele and Vidal (2017); Nguyen and Hein (2017); Safran and Shamir (2018); Nguyen and Hein (2018); Laurent and Brecht (2018)), have focused on the validity of these properties in different deep learning settings. Nonetheless, to our knowledge,\n9https://github.com/tensorflow/tensorflow/tree/master/tensorflow/ examples/tutorials/mnist\n10As opposed to the dataset used in our experiments with linear networks, measuring the training loss with MNIST is non-trivial computationally (involves passing through 60K examples). Therefore, rather than continuously polling training loss until it reaches a certain threshold, in this experiment we chose to evaluate speed of convergence by measuring the training loss once after a predetermined number of iterations. the success of landscape-driven analyses in formally proving convergence to global minimum for a gradient-based algorithm, has thus far been limited to shallow (two layer) models only (e.g. Ge et al. (2016); Du and Lee (2018); Du et al. (2018a)). An alternative to the landscape approach is a direct analysis of the trajectories taken by the optimizer.", "turbo-16k-0613_pred": "In recent years, deep learning has gained popularity due to its ability to solve complex problems using gradient-based optimization methods. While there have been efforts to mathematically analyze the convergence properties of deep learning algorithms, there are still limitations in proving convergence for deep neural networks. One approach, called the landscape approach, focuses on the properties of critical points in the objective function. It has been shown that for deep networks, the landscape approach suffers from inherent limitations in proving convergence. Another approach, called the trajectory-based approach, takes into account the trajectories taken by the optimizer during training. This approach has shown promising results for linear neural networks and matrix factorization problems. In this study, the authors propose a trajectory-based analysis of gradient descent for general deep linear neural networks. The analysis includes both the residual setting and other settings that better align with practical deep learning scenarios. The authors show that under certain conditions, including approximate balancedness and sufficient initialization, gradient descent converges to the global minimum at a linear rate. The analysis is further supported by experimental results in various deep learning settings. Overall, this study provides insights into the convergence properties of deep learning algorithms and highlights the importance of initialization and trajectory-based analysis in understanding and improving optimization in deep neural networks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper studies the convergence of gradient descent on the squared loss of deep linear neural networks. The authors prove linear convergence rate if (1) the network dimensions are big enough so that the full product can have full rank, (2) the singular values of each weight matrices are approximately the same, (3) the initialized point is \u201cclose enough\u201d to the target.\n\nMy main concern about the study of GD on linear neural network is whether we really get any \u201cbenefit\u201d or \u201cacceleration\u201d from depth, i.e., is GD on linear neural nets any faster than GD on linear models. It\u2019s been shown that we get acceleration in some cases (e.g., $\\ell_p$ regression when $p>2$ [Arora et al. 18\u2019]), but some other results (e.g., [Shamir 18\u2019] mentioned in Section 5) show that GD on linear neural nets (when weight matrices are all scalar) suffer exponential (in depth) increase in convergence time at near zero region, due to the vanishing gradient phenomenon. From my understanding, this paper circumvents this problem by assuming deficiency margin, because in the setting of [Shamir 18\u2019], deficiency margin means that the initialized product ($W_{1:N}$) has the same sign as $\\Phi$ and far enough from zero, so we don\u2019t have to pass through the near-zero region.\n\nBelow, I\u2019ll list specific concerns/questions/comments.\n* In my opinion, the statements about \u201cnecessity\u201d of two key assumptions are too strong, because the authors only provide counterexamples of non-convergence. As [Theorem 3, Shamir 18\u2019] shows (although in scalar case), even when the assumptions are not satisfied, a convergence rate $O(exp(N) * log(1/\\epsilon))$ is possible. It will be an interesting future work to clearly delineate the boundary between convergence and non-convergence.\n\n* In Thm 2 and Claim 3, what happens if dimension $d_0$ is smaller? What is the reason that you had to restrict it to high dimension? Is it due to high variance with few samples?\n\n* In Thm 2, constants $d\u2019_0$ and $a$ hide the dependence of the result on p, but I would suggest stating the dependence of those parameters on p, and also dependence on other parameters such as N.\n\n* In Section 5, there is a statement \u201cThis negative result, a theoretical manifestation of the \u201cvanishing gradient problem\u201d, is circumvented by balanced initialization.\u201d Can you elaborate more on that? If my understanding is correct, there is still $\\sigma_min$ multiplier in Eq (9), which means that at near-zero regions, the gradient will still vanish.\n\n7: Good paper, accept", "turbo-16k-0613_pred": "The paper presents a convergence analysis of gradient descent for deep linear neural networks. It focuses on two key assumptions: approximate balancedness of the weight matrices at initialization and positive deficiency margin of the end-to-end matrix with respect to the target. The authors prove that under these assumptions, gradient descent converges to the global minimum at a linear rate. They also show that a balanced initialization, which ensures both approximate balancedness and deficiency margin, leads to improved convergence. Experimental results support the theoretical findings, demonstrating the benefits of balanced initialization in practice. The paper concludes by highlighting the potential application of the analysis to deep non-linear neural networks and the importance of careful initialization in deep learning. Overall, the paper provides valuable insights into the convergence properties of deep linear neural networks and suggests a promising direction for future research. Some suggestions for improvement include discussing the limitations of the analysis and providing a more detailed comparison with related work. Additionally, it would be beneficial to address the scalability of the method and potential extensions to other types of neural networks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced and a differentiable Kullback-Leibler divergence approximation for this prior is derived. After training with Variational Network Quantization, weights can be replaced by deterministic quantization values with small to negligible loss of task accuracy (including pruning by setting weights to 0). The method does not require fine-tuning after quantization. Results are shown for ternary quantization on LeNet-5 (MNIST) and DenseNet (CIFAR-10).", "turbo-16k-0613_pred": "This paper presents Variational Network Quantization (VNQ), a Bayesian network compression method that combines pruning and few-bit quantization of weights. The authors introduce a multi-modal quantizing prior that penalizes weights of low variance unless they lie close to one of the target values for quantization. The method yields a Bayesian neural network with a multi-modal posterior over weights, which is used for subsequent pruning and quantization. After pruning and quantization, the method produces a deterministic feed-forward neural network with heavily quantized weights. The paper demonstrates the effectiveness of VNQ on the LeNet-5 (MNIST) and DenseNet (CIFAR-10) architectures. Experimental results show that VNQ achieves high compression rates with minimal loss in task accuracy.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Uncertainty in deep learning , Authors: Yarin Gal , 2016\n2.  Title: Bayesian compression for deep learning , Authors: Christos Louizos Karen Ullrich... , 2017\n3.  Title: Variational dropout sparsifies deep neural networks , Authors: Dmitry Molchanov Arsenii Ashukha... , 2017\n4.  Title: Structured bayesian pruning via log-normal multiplicative noise , Authors: Kirill Neklyudov Dmitry Molchanov... , 2017\n5.  Title: Soft weight-sharing for neural network compression , Authors: Karen Ullrich Edward Meeds... , 2017\n6.  Title: Trained ternary quantization , Authors: Chenzhuo Zhu Song Han... , 2016\n7.  Title: 2017) presented an approximation for the KL divergence under a (zero-centered) log uniform prior (Eq. (5)). Since our quantizing prior is essentially a composition of shifted log uniform priors, we construct a composition of the approximation given by Molchanov et al , Authors: Molchanov , 2017\n", "gt": "Our method is an extension of Sparse VD (Molchanov et al., 2017), originally used for network pruning. In contrast, we use a quantizing prior, leading to a multi-modal posterior suitable for fewbit quantization and pruning. Bayesian Compression and Structured Bayesian Pruning (Louizos et al., 2017; Neklyudov et al., 2017) extend Sparse VD to prune whole neurons or filters via groupsparsity constraints. Additionally, in Bayesian Compression the required bit-precision per layer is determined via the posterior variance. In contrast to our method, Bayesian Compression does not explicitly enforce clustering of weights during training and thus requires bit-widths in the range between 5 and 18 bits. Extending our method to include group-constraints for pruning is an interesting direction for future work. Another Bayesian method for simultaneous network quantization and pruning is soft weight-sharing (SWS) (Ullrich et al., 2017), which uses a Gaussian mixture model prior (and a KL term without trainable parameters such that the KL term reduces to the prior entropy). SWS acts like a probabilistic version of k-means clustering with the advantage of automatic collapse of unnecessary mixture components. Similar to learning the codebooks in our method, soft weight-sharing learns the prior from the data, a technique known as empirical Bayes. We cannot directly compare against soft weight-sharing since the authors do not report results on ternary networks. Gal et al. (2017) learn dropout rates by using a continuous relaxation of dropout\u2019s discrete masks (via the concrete distribution). The authors learn layer-wise dropout rates, which does not allow for dropout-rate-based pruning. We experimented with using the concrete distribution for learning codebooks for quantization with promising early results but so far we have observed lower pruning rates or lower accuracy compared to VNQ. A non-probabilistic state-of-the-art method for network ternarization is Trained Ternary Quantization (Zhu et al., 2016) which uses fullprecision shadow weights during training, but quantized forward passes.", "turbo-16k-0613_pred": "Related Work\n\nSeveral papers have explored different approaches to network compression and pruning. Gal (2016) investigated the use of uncertainty in deep learning and its potential for compression. Louizos et al. (2017) proposed Bayesian compression for deep learning, which learns a posterior distribution over network weights under a sparsity-inducing prior. Molchanov et al. (2017) introduced variational dropout, which sparsifies deep neural networks by learning dropout noise levels per weight. Neklyudov et al. (2017) extended this approach to structured Bayesian pruning using log-normal multiplicative noise. Ullrich et al. (2017) presented soft weight-sharing for neural network compression, while Zhu et al. (2016) explored trained ternary quantization. Another relevant paper by Molchanov et al. (2017) developed an approximation for the KL divergence under a log uniform prior, which is relevant to our quantizing prior in this work. These papers collectively shed light on different techniques that can be used in network compression and pruning tasks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper presented a straightforward extension of the work done at [1, 2] for ternary networks through a multimodal quantising prior. It is generally well-written, with extensive preliminaries and clear equations. The visualizations also serve as a nice way to convey the behaviour of the proposed approach. The idea is interesting and well executed so I propose for acceptance. I only have a couple of minor questions: \n- For the KL-divergence approximation you report a maximum difference of 1 nat per weight that seems a bit high; did you experiment with the `naive` Monte Carlo approximation of the bound (e.g. as done at Bayes By Backprop) during optimization? If yes, was there a big difference in performance?\n- Was pre-training necessary to obtain the current results for MNIST? As far as I know, [1] and [2] did not need pre-training for the MNIST results (but did employ pre-training for CIFAR 10).\n- How necessary was each one of the constraints during optimization (and what did they prevent)? \n- Did you ever observe posterior means that do not settle at one of the prior modes but rather stay in between? Or did you ever had issues of the variance growing large enough, so that q(w) captures multiple modes of the prior (maybe the constraints prevent this)? How sensitive is the quantisation scheme?\n\nOther minor comments / typos:\n(1) 7th line of section 2.1 page 2, \u2018a unstructured data\u2019 -> \u2018unstructured data\u2019\n(2) 5th line on page 3, remove \u2018compare Eq. (1)\u2019 (or rephrase it appropriately).\n(3) Section 2.2, \u2019Kullback-Leibler divergence between the true and the approximate posterior\u2019; between implies symmetry (and the KL isn\u2019t symmetric) so I suggest to change it to e.g. \u2018from the true to the approximate posterior\u2019 to avoid confusion. Same for the first line of Section 3.3.\n(4) Footnote 2, the distribution of the noise depends on the random variable so I would suggest to change it to a general \\epsilon \\sim p(\\epsilon).\n(5) Equation 4 is confusing.\n\n7: Good paper, accept", "turbo-16k-0613_pred": "In this paper, the authors propose a Bayesian network compression method called Variational Network Quantization (VNQ). The method aims to simultaneously prune and quantize weights in a neural network by introducing a multi-modal quantizing prior. The authors extend previous Bayesian pruning methods and use a variational approximation to learn a posterior distribution over weights. They demonstrate the effectiveness of their method on two datasets, MNIST and CIFAR-10, using LeNet-5 and DenseNet models respectively.\n\nOverall, the paper is well-written and provides a clear explanation of the proposed method. The authors provide thorough experimental results, demonstrating that VNQ achieves high pruning rates with minimal loss in accuracy. The visualization of weight distributions after VNQ training provides insight into the effectiveness of the method. \n\nHowever, there are a few suggestions and questions that could be addressed in the paper. First, it would be beneficial to provide a more detailed explanation of the KL approximation used in the method and how it compares to other approximation methods. Additionally, it would be helpful to discuss the limitations of the method and potential areas for future research. Finally, it would be interesting to see a comparison of VNQ with other state-of-the-art compression methods to further evaluate its performance and effectiveness.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework. We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment. We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance. Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previouslyseen tasks to substantially improve their own learning efficiency.", "turbo-16k-0613_pred": "This paper introduces the TouchStream environment, a continual reinforcement learning framework that allows agents to learn and switch between different tasks. The authors propose a neural module approach called Reward Map Prediction (ReMaP), which uses task-specialized decision modules built on top of a fixed sensory system. The sensory system computes a general-purpose visual representation, while the decision modules are lightweight and flexible, allowing for efficient learning and switching between tasks. The authors also introduce a dynamic neural voting scheme, which allows agents to repurpose knowledge from previously learned tasks to improve learning efficiency in new tasks. They conducted experiments using the TouchStream environment and showed that their approach achieved high levels of performance and learning efficiency across various tasks. The study also explored various ablations and evaluated the effectiveness of the dynamic voting controller in task-switching scenarios. Overall, the results demonstrate the potential of the proposed framework and algorithms for enabling flexible and efficient learning in complex real-world environments.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Deep compositional question answering with neural module , Authors: Jacob Andreas Marcus Rohrbach... , 2015\n2.  Title: Net2net: Accelerating learning via knowledge transfer , Authors: Tianqi Chen Ian Goodfellow... , 2015\n3.  Title: Pathnet: Evolution channels gradient descent in super neural networks , Authors: Chrisantha Fernando Dylan Banarse... , 2017\n4.  Title: Learning to Reason: End-to-End Module Networks for Visual Question Answering , Authors: R. Hu J. Andreas... , 2017\n5.  Title: Overcoming catastrophic forgetting , Authors: James Kirkpatrick Razvan Pascanu... , 2016\n6.  Title: ImageNet classification with deep convolutional neural networks , Authors: A Krizhevsky I Sutskever... , 2012\n7.  Title: Learning to reinforcement learn , Authors: Jane X. Wang Zeb Kurth-Nelson... , 2016\n8.  Title: Improved multitask learning through synaptic intelligence , Authors: Friedemann Zenke Ben Poole... , 2017\n9.  Title: There are a total of 59 unique classes in this dataset , Authors: Yamins , 2014\n", "gt": "Modern deep convolutional neural networks have had significant impact on computer vision and artificial intelligence (Krizhevsky et al., 2012), as well as in the computational neuroscience of vision (Yamins & DiCarlo (2016)). There is a recent but growing literature on convnet-based neural modules, where they have been used for solving compositional visual reasoning tasks (Andreas et al., 2015; Hu et al., 2017). In this work we apply the idea of modules to solving visual learning challenges in a continual learning context. Existing works rely on choosing between a menu of pre-specified module primitives, using different module types to solve subproblems involving specific input-output datatypes, without addressing how these modules\u2019 forms are to be discovered in the first place. In this paper, we show a single generic module architecture is capable of automatically learning to solve a wide variety of different tasks in a unified action/state space, and a simple controller scheme is able to switch between such modules. Our results are also closely connected with the literature on lifelong (or continual) learning (Kirkpatrick et al., 2016; Rusu et al., 2016). A part of this literature is concerned with learning to solve new tasks without catastrophically forgetting how to solve old ones (Zenke et al., 2017; Kirkpatrick et al., 2016). The use of modules obviates this problem, but instead shifts the hard question to one of how newly-allocated modules can be learned effectively. The continual learning literature also directly addresses knowlege transfer to newly allocated structures (Chen et al., 2015; Rusu et al., 2016; Fernando et al., 2017), but largely addresses how transfer learning can lead to higher performance, rather than addressing how it can improve learning speed. ", "turbo-16k-0613_pred": "Related Work\nIn recent years, there has been significant research on neural modules and their application in various tasks. One notable work is the study on neural module networks for compositional question answering, where the authors proposed a framework for combining pre-existing neural networks into a larger network by having them act as modules (Andreas et al., 2015). This approach allows for the reusability of modules across different tasks and promotes transfer learning. Another relevant paper is the one on net2net, which focuses on transferring knowledge from a smaller neural network to a larger one, resulting in accelerated learning (Chen et al., 2015). This technique could prove beneficial in the context of the dynamic neural voting system, as it allows for the transfer of knowledge and efficient learning. Building upon these ideas, the authors of Pathnet introduced a system where evolution channels gradient descent in super neural networks, enabling knowledge transfer and efficient learning across modules (Fernando et al., 2017). This concept of evolution and transfer learning can be applied to the dynamic neural voting system to enhance its performance. Furthermore, there has been research on overcoming catastrophic forgetting, which is highly relevant in the context of continual learning. One approach to address this issue is through methods like elastic weight consolidation, which places penalties on updates to important weights to prevent forgetting (Kirkpatrick et al., 2016). Another approach is synaptic intelligence, which uses an algorithm to prioritize important weights during training (Zenke et al., 2017). These methods could be employed to improve the performance of the dynamic voting controller in the context of task switching and continual learning. Additionally, the use of deep convolutional neural networks for image classification has been extensively studied, as demonstrated by the well-known ImageNet classification project (Krizhevsky et al., 2012). This research has paved the way for advancements in computer vision tasks, including the TouchStream environment described in this paper.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "Reading this paper feels like reading at least two closely-related papers compressed into one, with overflow into the appendix (e.g. one about the EMS module, one about the the recurrent voting, etc).\n\nThere were so many aspects/components, that I am not entirely confident I fully understood how they all work together, and in fact I am pretty confident there was at least some part of this that I definitely did not understand. Reading it 5-20 more times would most likely help.\n\n-The defs/eqns at the beginning of section 3.1 (Sc, CReLu, etc) were slightly hard to follow and I wonder whether there were any typos, e.g. was CReS meant to refer directly to Sc, but used the notation ${ReLu}^2$ instead? \n\nEach of these on its own would be easier to overlook, but there is a compounding effect here for me, as a reader, such that by further on in the paper, I am rather confused.\n\nI also wonder whether any of the elements described, have more \"standard\" interpretations/notations. For example, my slight confusion propagated further: after above point, I then did not have a clear intuition about $l_i$ in the EMS module. I get that symmetry has been built in, e.g. by the definitions of CReS and CReLu, etc, but I still don't see how it all works together, e.g. are late bottleneck architectures *exactly* the same as MLPs, but where inputs have simply been symmetrized, squared, etc? Nor do I have intuition about multiplicative symmetric interactions between visual features and actions, although I do get the sense that if I were to spend several hours implementing/writing out toy examples, it would clarify it significantly (in fact, I wouldn't be too surprised if it turns out to be fairly straightforward, as in my above comment indicating a seeming equivalence to simply multiplying two terms and taking the resulting sign). If the paper didn't need to be quite as dense, then I would suggest providing more elucidation for the reader, either with intuitions or examples or clearer relationships to more familiar formulations.\n\nLater, I did find that some of the info I *needed* in order to understand the results (e.g. exactly what is meant by a \"symmetry ablation\", how was that implemented?) was in fact in the appendices (of which there are over 8 pages).\n\nI do wonder how sensitive the performance of the overall system is to some of the details, like, e.g. the low-temp Boltzmann sampling rather than identity function, as described at the end of S2.\n\n8: Top 50% of accepted papers, clear accept", "turbo-16k-0613_pred": "In this paper, the authors propose a neural module approach to solve the problem of flexible learning and task switching in real-world environments. They introduce the TouchStream environment, a framework that unifies a wide variety of spatial decision-making tasks. The authors then present the Reward Map Prediction (ReMaP) algorithm, which uses a neural network to approximate the mapping between the action space and reward space. They also introduce a dynamic neural voting scheme for task switching, where a meta-controller assigns weights to different modules based on their performance. The authors conduct experiments to evaluate the efficiency of the proposed method and compare it to alternative architectures. Overall, the study shows promising results in terms of learning efficiency and task-switching capabilities, although further research is needed to address scalability and the ability to handle complex tasks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "The importance-weighted autoencoder (IWAE) approach of Burda et al. (2015) defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. (2017) reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variational distributions. In this work, we provide yet another perspective on the IWAE bounds. We interpret each IWAE bound as a biased estimator of the true marginal likelihood where for the bound defined on K samples we show the bias to be of order O(K\u22121). In our theoretical analysis of the IWAE objective we derive asymptotic bias and variance expressions. Based on this analysis we develop jackknife variational inference (JVI), a family of bias-reduced estimators reducing the bias to O(K\u2212(m+1)) for any given m < K while retaining computational efficiency. Finally, we demonstrate that JVI leads to improved evidence estimates in variational autoencoders. We also report first results on applying JVI to learning variational autoencoders.1", "turbo-16k-0613_pred": "In this paper, the authors propose a new method called Jackknife Variational Inference (JVI) for estimating evidence in variational autoencoders (VAE). JVI is based on classic higher-order bias removal techniques, specifically the jackknife method, and aims to reduce biases in evidence estimation. The authors show that JVI produces more accurate estimates of the marginal likelihood compared to other methods such as the evidence lower-bound (ELBO) and importance weighted autoencoders (IWAE). They also demonstrate that higher-order bias reduction is more effective than lower-order bias reduction. Experimental results on the MNIST dataset validate the effectiveness of JVI in reducing bias and providing more accurate evidence estimates. However, they also find that using JVI as a training objective does not lead to improvements over IWAE. The authors suggest exploring the use of other resampling methods for bias reduction and improving computational efficiency. Overall, JVI offers a simple and efficient approach to reducing biases in evidence estimation in VAEs.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Perturbative black box variational inference , Authors: Robert Bamler Cheng Zhang... , 2017\n2.  Title: Nice: Non-linear independent components estimation , Authors: Laurent Dinh David Krueger... , 2014\n3.  Title: Adversarial message passing for graphical models , Authors: Theofanis Karaletsos , 2016\n4.  Title: Improving variational inference with inverse autoregressive flow , Authors: Diederik P Kingma Tim Salimans... , 2016\n5.  Title: Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks , Authors: Lars Mescheder Sebastian Nowozin... , 2017\n6.  Title: Masked autoregressive flow for density estimation , Authors: George Papamakarios Iain Murray... , 2017\n7.  Title: On the opportunities and pitfalls of nesting monte carlo estimators , Authors: Tom Rainforth Robert Cornish... , 2017\n8.  Title: Tighter variational bounds are not necessarily better , Authors: Tom Rainforth Tuan Anh Le... , 2017\n9.  Title: Markov chain monte carlo and variational inference: Bridging the gap , Authors: Tim Salimans Diederik Kingma... , 2015\n10.  Title: A collapsed variational bayesian inference algorithm for latent dirichlet allocation , Authors: Yee W Teh David Newman... , 2007\n11.  Title: Deep and hierarchical implicit models , Authors: Dustin Tran Rajesh Ranganath... , 2017\n", "gt": "The IWAE bound and other Monte Carlo objectives have been analyzed by independently by Rainforth et al. (2017a). Their analysis is more general than our IWAE analysis, but does not propose a method to reduce bias. Delta-method variational inference (DVI) proposed by Teh et al. (2007) is the closest method we are aware of and we discuss it in detail as well as provide novel results in Appendix B, page 14. Another exciting recent work is perturbative variational inference (Bamler et al., 2017) which considers different objective functions for variational inference; we are not sure whether there exists a deeper relationship to debiasing schemes. There also exists a large body of work that uses the ELBO objective but considers ways to enlarge the variational family. This is useful because the larger the variational family, the smaller the bias. Non-linear but invertible transformations of reference densities have been used initially for density estimation in NICE (Dinh et al., 2014) and for variational inference in Hamiltonian variational inference (Salimans et al., 2015). Around the same time the general framework of normalizing flows (Rezende & Mohamed, 2015) unified the previous works as some invertible continuous transformation of a distribution. Since then a large number of specialized flows with different computational requirements and flexibility have been constructed: inverse autoregressive flows (Kingma et al., 2016), masked autoregressive flows Papamakarios et al. (2017), and Householder flows (Tomczak & Welling, 2016). Another way to improve the flexibility of the variational family has been to use implicit models (Mohamed & Lakshminarayanan, 2016) for variational inference; this line of work includes adversarial variational Bayes (Mescheder et al., 2017), wild variational inference (Li & Liu, 2016), deep implicit models (Tran et al., 2017), implicit variational models (Husza\u0301r, 2017), and adversarial message passing approximations (Karaletsos, 2016).", "turbo-16k-0613_pred": "In recent years, variational autoencoders (VAEs) have become a popular tool for generative modeling, representation learning, and probabilistic regression. However, VAEs suffer from limitations such as the quality of the model used to approximate the true distribution and the lower-bound nature of the evidence lower-bound (ELBO). To address these limitations, researchers have explored methods like importance weighted autoencoders (IWAE) and delta method variational inference (DVI).\n\nIWAE extends the VAE objective by introducing an integer K, which represents the number of importance samples. This allows for a tighter lower-bound on the true likelihood. In particular, the empirical version of the IWAE objective, denoted as L\u0302K, enhances the performance of VAEs by reducing bias and providing better estimates of the marginal likelihood. The bias reduction is achieved by combining different IWAE estimates through a modified distribution. This unbiased estimate can be further generalized using the jackknife methodology.\n\nThe jackknife variational inference (JVI) method, a form of higher-order bias removal, improves upon the biases introduced by the IWAE estimator. By using subsets of the original samples and applying the jackknife methodology, JVI achieves reduced bias while controlling variance. Additionally, JVI estimators up to order five have been shown to significantly reduce bias and improve evidence estimates.\n\nIn terms of computational complexity, JVI can be efficiently computed using GPU hardware. By converting the algorithm into matrix operations, the runtime is minimized and the complexity is reduced to O(K). Although variations of JVI that evaluate only a fraction of all possible subsets can also be explored, they may result in increased variance.\n\nExperimental results on MNIST data demonstrate the superiority of JVI over ELBO and IWAE in terms of evidence estimation. The quality of evidence estimates improves monotonically with the order of the JVI estimator, and the standard error remains consistent across all JVI estimates.\n\nIn conclusion, JVI offers a simple and efficient solution for bias reduction in evidence estimation. While further exploration of resampling methods like the iterated bootstrap or debiasing lemma may lead to more improvements, JVI provides a promising approach to enhancing variational inference models.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors introduce jackknife variational inference (JVI), a method for debiasing Monte Carlo objectives such as the importance weighted auto-encoder. Starting by studying the bias of the IWAE bound for approximating log-marginal likelihood, the authors propose to make use of debiasing techniques to improve the approximation. For the binarized MNIST the authors show improved approximations given the same number of samples from the auxiliary distribution q(z|x).\n\nJVI seems to be an interesting extension of, and perspective on, the IWAE bound (and other Monte Carlo objectives). Some questions and comments:\n\n* The Cremer et al. (2017) paper contains some errors when interpreting the IWAE bound as a standard ELBO with a more flexible variational approximation distribution. For example eq. (1) in their paper does not correspond to an actual distribution, it is not properly normalized. This makes the connection in their section 2.1 unclear. I would suggest citing the following paper instead for this connection and the relation to importance sampling (IS):\nNaesseth, Linderman, Ranganath, Blei, \"Variational Sequential Monte Carlo\", 2017.\n\n* Regarding the analysis of the IWAE bound the paper by Rainforth et al. (2017) mentioned in the comments seems very relevant. Also, because of the strong connection between IWAE and IS detailed in the Naesseth et al. (2017) paper it is possible to make use of a standard Taylor approximation/delta methods to derive Prop. 1 and Prop. 2, see e.g. Robert & Casella, \"Monte Carlo Statistical Methods\" or Liu's \"Monte Carlo Strategies for Scientific Computing\".\n\n* It could be worth mentioning that the JVI objective function is now no longer (I think?) a lower bound to the log-evidence.\n\n* Could the surprising issue (IWAE-learned, JV1-evaluated being better than JV1-learned, JV1-evaluated) in Table 1 be because of different local optima?\n\n* Also, we can easily get unbiased estimates of the evidence p(x) using IS and optimize this objective wrt to model parameters. The proposal parameters can be optimized to minimize variance, how do you think this compares to the proposed method?\n\nMinor comments:\n* p(x) -> p_\\theta(x)\n* In the last paragraph of section 1 it seems like you claim that the expressiveness of p_\\theta(x|z) is a limitation of VAE. It was a bit unclear to me what was actually a general limitation of maximum likelihood versus the approximation based on VAEs.\n* Last paragraph of section 1, \"strong bound\" -> \"tight bound\"\n* Last paragraph of section 2, citation missing for DVI\n\n7: Good paper, accept", "turbo-16k-0613_pred": "In this paper, the authors propose a new method called Jackknife Variational Inference (JVI) for estimating the marginal likelihood of generative models. They leverage classic higher-order bias removal techniques to improve the accuracy of evidence estimates. The JVI method is computationally efficient and provides better estimates of the marginal likelihood compared to existing approaches such as Variational Autoencoders (VAE) and Importance Weighted Autoencoders (IWAE).\n\nThe authors conducted experiments on the MNIST dataset using VAE trained with different objectives, including ELBO, IWAE, and JVI. They evaluated the accuracy of evidence estimates and found that higher-order JVI estimators outperform IWAE estimates by more than one order of magnitude. The higher-order JVI estimators also showed a monotonic improvement in accuracy with increasing order.\n\nThe authors also explored using JVI as a training objective for VAE models. Preliminary results showed that while JVI improved evidence estimates, it did not necessarily lead to better model learning compared to IWAE.\n\nOverall, the JVI method provides a promising approach for estimating marginal likelihood and reducing bias in generative models. Future work could explore the use of other resampling methods for bias reduction and investigate the impact of different learning objectives on model performance.\n\nMy suggestions for the authors would be to investigate the discrepancy between improved evidence estimates and model learning when using JVI as a training objective. It would also be interesting to explore the use of other bias reduction techniques, such as the iterated bootstrap or debiasing lemma. Additionally, providing a more detailed analysis of the variance reduction properties of JVI would further enhance the understanding of this method.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied. Consider, for example, the trade-off between thermal resistance, electrical conductivity, and mechanical stability needed to design a nano-porous template with optimal thermoelectric efficiency. To that end, we leverage the posterior regularization framework and show that this constraint satisfaction problem can be formulated as sampling from a Gibbs distribution. The main challenges come from the black-box nature of those physical constraints, since they are obtained via solving highly non-linear PDEs. To overcome those difficulties, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling. We explore two surrogate approaches. The first approach exploits zero-order approximation of gradients in the Langevin Sampling and we refer to it as Zero-Order Langevin. In practice, this approach can be prohibitive since we still need to often query the expensive PDE solvers. The second approach approximates the gradients in the Langevin dynamics with deep neural networks, allowing us an efficient sampling strategy using the surrogate model. We prove the convergence of those two approaches when the target distribution is log-concave and smooth. We show the effectiveness of both approaches in designing optimal nano-porous material configurations, where the goal is to produce nano-pattern templates with low thermal conductivity and reasonable mechanical stability.", "turbo-16k-0613_pred": "In this paper, the authors propose a Surrogate-Based Constrained Langevin Sampling approach for black-box sampling from a Gibbs distribution. The goal is to design optimal nano-configurations under thermal conductivity and mechanical stability constraints. The authors compare different surrogate models, including regression, Taylor-Reg, Taylor-1, and zero-order, in terms of their effectiveness in approximating the gradient of the potential function. They also demonstrate the usability of their approach in generating new samples that satisfy multiple constraints. Experimental results show that the zero-order models achieve the lowest thermal conductivity but are computationally expensive, while the deep surrogate models offer a good trade-off between accuracy and computation time. The authors also propose a hybrid algorithm that combines the best of both approaches. The proposed method shows promise for the design of nano-material configurations and offers a more efficient alternative to existing techniques.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Sampling from a log-concave distribution with compact support with proximal langevin monte carlo , Authors: Nicolas Brosse Alain Durmus... , 2017\n2.  Title: Finite-time analysis of projected langevin monte carlo , Authors: Sebastien Bubeck Ronen Eldan... , 2015\n3.  Title: Kernel exponential family estimation via doubly dual embedding , Authors: Bo Dai Hanjun Dai... , 2018\n4.  Title: Optimal rates for zero-order convex optimization: The power of two function evaluations , Authors: John C Duchi Michael I Jordan... , 2015\n5.  Title: Mirrored langevin dynamics , Authors: Ya-Ping Hsieh Ali Kavis... , 2018\n6.  Title: Estimation of non-normalized statistical models by score matching , Authors: Aapo Hyv\u00e4rinen , 2005\n7.  Title: Non-asymptotic results for langevin monte carlo: Coordinate-wise and black-box sampling , Authors: Lingqing Shen Krishnakumar Balasubramanian... , 2019\n8.  Title: Under review as a conference paper at ICLR , Authors: Qiang Wu Justin Guinney... , 2020\n9.  Title: The proof here is similar to the proof of Lemma 8 in (Bubeck et al., 2015). By the data processing inequality we have: KL(\u03bcT , Authors:  , 2015\n", "gt": "Zero-Order Methods. Zero-order optimization with Gaussian smoothing was studied in Nesterov & Spokoiny (2017) and Duchi et al. (2015) in the convex setting. Non-convex zero order optimization was also addressed in Ghadimi & Lan (2013). The closest to our work is the zero-order Langevin Shen et al. (2019) introduced recently for black-box sampling from log concave density. The main difference in our setting, is that the density has a compact support and hence the need to appeal to projected LMC (Bubeck et al., 2015) and Proximal LMC (Brosse et al., 2017). It is worth nothing that Hsieh et al. (2018) introduced recently mirror Langevin sampling that can also be leveraged in our framework. Gradients and Score functions Estimators. We used the approach of gradient distillation (Srinivas & Fleuret, 2018) and learning gradients of (Wu et al., 2010), since they are convenient for training on different constraints and they come with theoretical guarantees. However, other approaches can be also leveraged such as the score matching approach for learning the gradient of the log likelihood (Hyv\u00e4rinen, 2005) and other variants appealing to dual embeddings (Dai et al., 2018). Estimating gradients can be also performed using Stein\u2019s method as in (Li & Turner, 2017), or via maintaining a surrogate of the gradient as in Stein descent without gradient (Han & Liu, 2018). Optimization approaches. Due to space limitation, we restrict the discussion to the optimization methods that are most commonly and recently used for optimal material (or molecule) design.", "turbo-16k-0613_pred": "Related Work\n\nSeveral works have addressed the problem of sampling from log-concave distributions with compact support using Langevin dynamics. Brosse et al. (2017) introduced the Proximal Langevin Monte Carlo (PLMC) algorithm, which has been shown to have desirable mixing properties. Bubeck et al. (2015) studied the Projected Langevin Monte Carlo (PLMC) algorithm and proved its convergence to the target distribution. Hyv\u00e4rinen (2005) developed the score matching method for estimating non-normalized statistical models. Duchi et al. (2015) analyzed the optimal rates for zero-order convex optimization. Hsieh and Kavis (2018) proposed the Mirrored Langevin Dynamics algorithm, which combines PLMC and a reflection technique to improve convergence. Shen et al. (2019) presented non-asymptotic results for Langevin Monte Carlo, including coordinate-wise and black-box sampling. Dai et al. (2018) introduced kernel exponential family estimation using doubly dual embedding. Wu et al. (2020) developed a novel approach for non-asymptotic analysis of Langevin dynamics. The proof for the convergence of the surrogate Langevin dynamics in this paper is similar to the proof of Lemma 8 in Bubeck et al. (2015), using the data processing inequality. These previous works provide important theoretical foundations and algorithms for sampling from log-concave distributions with compact support using Langevin dynamics.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper considers the problem of sampling points from a constrained set in R^d where the constraints can only be accessed in a zero order fashion. They consider the specific situation where the constraints are a solution of a complicated PDE solver and hence the derivatives or specific functional forms of the constraint cannot be obtained. They repose the problem as sampling from a Gibbs distribution whose potential contains constraints as penalties in a Lagrangian fashion. They now wish to sample from the Gibbs distribution using Langevin diffusion.  The Langevin process requires a derivative of the gradient. The setting does not allow for that and therefore the authors propose two approaches - \n\n1. Constructing the gradient from zeroth order entries of a gaussian smoothed potential (much like works of Nesterov et al on zero order optimization). \n2. Using a parameteric function class (like an RKHS or a neural network) to learn a function which well approximates the gradient of the constraints as well given zeroth order constraint evaluations. \n\n\nThe theoretical analysis seems quite rigorous as it begins by first providing a basic guarantee for constrained langevin sampling when gradients are computed with error. The non error gradient part of this analysis has been established before and the authors mention the references appropriately. I have a couple of questions regarding the precise statements of the theorem that i will ask towards the end of the review. Overall its hard to comment on the tightness of the analysis as the non-error versions are also unclear of the tightness of the bounds. Nevertheless the bounds achieved do not look much worse than the non-error counterparts and are easy to implement. The rest of the bounds focus on achieving low error in approximation of the gradients in various settings. Overall the theory in this part seems very loose in terms of bounds as exponential factors in dimensions start to appear and in that regard seems quite preliminary but its hard to comment on whether its natural or can be improved. \n\nSome questions I have regarding the theorem statements \n\nRegarding theorem 1 SPLMC convergence  (and corollaries of the theorem) -  I find it surprising that there is no lower bound assumption on eta in terms of K - only an upper bound. This seems wrong particularly as the theorem as stated then allows eta to be set extremely small while K is finite, in which case there should be no convergence theorems at all. The condition on eta should be a theta(f (K)) for some f type of condition like in the second part of the theorem. I would suggest the authors to look into the theorem - or claify why this is the case. \n\nI am confused by the presentation of the Shi et al results as there is no penalty appearing for the approximation error due to an RKHS, only a finite sample penalty. Does the result assume that phi belongs to the function class of the RKHS in question? Probably yes and in that case that should be specified. \n\n6: Weak Accept", "turbo-16k-0613_pred": "Title: Review of \"Surrogate-Based Constrained Langevin Sampling for Design Problems\"\n\nSummary:\nThe paper introduces Surrogate-Based Constrained Langevin Sampling for black-box sampling from a Gibbs distribution. The authors propose two approaches for defining the surrogate gradient: zero-order optimization and learning gradient approximations using deep neural networks. Theoretical proofs for convergence are provided. The proposed method is applied to the design of nanoporous configurations with constraints on thermal conductivity and mechanical stability. Experimental results show the effectiveness of the surrogate models in approximating the potential function and generating samples that satisfy the constraints. The trade-off between computation and accuracy is discussed, with the hybrid method being suggested as a good compromise. Some additional experiments and proofs are included in the supplementary material.\n\nReview:\nThis paper addresses the problem of sampling from a Gibbs distribution with multiple constraints in a black-box setting. The proposed Surrogate-Based Constrained Langevin Sampling method offers an efficient approach to generate samples that satisfy the constraints. The use of surrogate models, either through zero-order optimization or gradient learning with neural networks, provides a good trade-off between accuracy and computation time. The experimental results on nanoporous configurations demonstrate the effectiveness of the method in finding optimal designs.\n\nOne suggestion I have is to provide more information on the computational cost of each approach, such as the number of iterations and the time required for convergence. This would provide a clearer understanding of the trade-off between accuracy and computation. Additionally, more details on the experimental setup, such as the size of the training dataset and the specific neural network architectures used, would be helpful for replication.\n\nOverall, the paper is well-written and the proposed method is sound. The theoretical proofs provide a solid foundation for the approach, and the experimental results support the effectiveness of the surrogate models. The paper could benefit from additional discussions on the limitations and potential extensions of the method.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "This work addresses continual learning for non-stationary data, using Bayesian neural networks and memory-based online variational Bayes. We represent the posterior approximation of the network weights by a diagonal Gaussian distribution and a complementary memory of raw data. This raw data corresponds to likelihood terms that cannot be well approximated by the Gaussian. We introduce a novel method for sequentially updating both components of the posterior approximation. Furthermore, we propose Bayesian forgetting and a Gaussian diffusion process for adapting to non-stationary data. The experimental results show that our update method improves on existing approaches for streaming data. Additionally, the adaptation methods lead to better predictive performance for non-stationary data.", "turbo-16k-0613_pred": "This paper proposes a method called Gaussian Residual Scoring (GRS) for online inference in Bayesian neural networks with non-stationary streaming data. The method consists of a memory update and a Gaussian update, which allows the model to adapt to changes in the data distribution. The memory update selects the most relevant data points based on a score function, while the Gaussian update sequentially updates the Gaussian distribution. The paper also introduces two adaptation methods: Bayesian exponential forgetting and adaptation through diffusion processes. Experimental results demonstrate the effectiveness of the GRS method and the adaptation methods in online learning and settings with concept drift. Overall, the paper provides valuable insights and techniques for continual learning in Bayesian neural networks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Streaming variational bayes , Authors: T. Broderick N. Boyd... , 2013\n2.  Title: Online variational Bayesian learning , Authors: Z. Ghahramani , 2000\n3.  Title: Overcoming catastrophic forgetting in neural networks , Authors: J. Kirkpatrick R. Pascanu... , 2017\n4.  Title: A practical Bayesian framework for backpropagation networks , Authors: D.J.C. MacKay , 1992\n5.  Title: Stochastic Models, Estimation and Control. Mathematics in science and engineering , Authors: P.S. Maybeck , 1982\n6.  Title: Virtual Vector Machine for Bayesian online classification , Authors: T.P. Minka R. Xiang... , 2009\n7.  Title: Variational continual learning , Authors: C.V. Nguyen Y. Li... , 2018\n8.  Title: A Bayesian approach to on-line learning , Authors: M. Opper , 1998\n9.  Title: Connectionist models of recognition memory: constraints imposed by learning and forgetting functions , Authors: R. Ratcliff , 1990\n10.  Title: Online structured laplace approximations for overcoming catastrophic forgetting , Authors: H. Ritter A. Botev... , 2018\n11.  Title: Catastrophic forgetting, rehearsal and pseudorehearsal , Authors: A. Robins , 1995\n12.  Title: Online model selection based on the variational bayes , Authors: M. Sato , 2001\n13.  Title: Continual learning with deep generative replay , Authors: H. Shin J.K. Lee... , 2017\n", "gt": "There are many Bayesian approaches to online learning, which differ mostly in the approximation of the posterior distribution at each time-step. Sequential Monte Carlo (Liu & Chen, 1998) approximates the posterior by a set of particles. Assumed Density Filtering (ADF) (Maybeck, 1982) and Bayesian online learning (Opper, 1998) are deterministic posterior approximations based on moment matching. Other deterministic approaches are based on Laplace\u2019s approximation (MacKay, 1992): Kirkpatrick et al. (2017) use multiple diagonal Gaussian posterior approximations of previous time-steps to regularise future tasks; Ritter et al. (2018) use a single (block-diagonal) posterior approximation, summarising all previous time-steps. The latter method is closer to Bayesian online inference, as it is an approximation of Eq. (1). Our work is based on online VB (Opper, 1998; Ghahramani, 2000; Sato, 2001; Broderick et al., 2013), which approximates the posterior at every time-step by minimising the KL-divergence between a parametric (here Gaussian) and the true posterior distribution. In contrast to online VB, we approximate the posterior by a Gaussian distribution and a running memory. Other approaches are based on various types of episodic memory, motivated by their empirical success in preventing catastrophic forgetting. The basic idea of rehearsal (Ratcliff, 1990) is to train on both the new data and a subset of previous data or pseudo samples (Robins, 1995; Shin et al., 2017; Kemker & Kanan, 2017) sampled from a generative model. The memory-based online inference methods most similar to our approach are VCL (Nguyen et al., 2018) and VVM (Minka et al., 2009). Both methods use a Gaussian distribution and a running memory to approximate the posterior. VCL uses heuristics such as random selection or the k-center method to update the memory.", "turbo-16k-0613_pred": "In recent years, there has been a growing interest in continual learning, also known as lifelong learning, which refers to the ability of computational systems to learn incrementally from a stream of data while avoiding catastrophic forgetting of previous knowledge. This field of research is motivated by the need for systems that can adapt to non-stationary data and changing data distributions. Traditional online multi-task learning approaches focus on preventing catastrophic forgetting of previously learned tasks, but they are not suitable for modeling non-stationary data.\n\nIn this paper, the authors propose a novel approach for training Bayesian neural networks incrementally with non-stationary streaming data. They develop an online approximation method using a diagonal Gaussian distribution and a running memory to represent the posterior distribution. The two components are sequentially updated while adapting to changes in the data distribution. The authors also introduce two alternative adaptation methods to handle non-stationary data. They compare their sequential update method to existing approaches on several popular datasets and demonstrate that their method is favorable. Furthermore, they validate their adaptation methods on datasets with concept drift and show performance improvements compared to existing methods without adaptation.\n\nOverall, this paper contributes to the field of continual learning by proposing a novel approach for training Bayesian neural networks with non-stationary data. The experiments conducted demonstrate the effectiveness of their approach in handling concept drift and adapting to changing data distributions. The authors acknowledge that further research is needed to address issues such as drift detection and extending the approximation family beyond Gaussians.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "1. Summary:\nThis proposes considers neural networks training with non-stationary streaming data. To address online inference, the paper uses variational online updates and a running memory (coreset) summarising the data points seen so far, as recently used in the variational continual learning approach. First, the paper identifies a gap in this recent approach that coreset point selection process does not take into account the approximation quality. The paper develops a sound procedure to select the memory points, essentially to identify \u201cdifficult\u201d data points under the current posterior and put them into the running memory. Second, to handle \u201cconcept drift\u201d, the paper considers imposing some decaying on the likelihood of past data and derives post-hoc variational posterior updates for this case. Two contributions are validated on some streaming regression and classification tasks.\n\n2. Opinion and rationales\n\nI\u2019m leaning towards \u201caccept\u201d for this paper since it presents two interesting contributions (albeit of incremental novelty)  to the approximate inference area, it has clear execution and super clean presentation, and the experiments clearly demonstrate the values of the proposed approaches.\n\nI would like the paper to clarify the following:\n\na. I find the objective for selecting memory points interesting, but am wondering if using individual predictability terms by themselves are good enough to select these points. Perhaps, after some thoughts, memory is perhaps not the right word to characterise these points. \n\nIt seems to me (i) these terms indicate how well the current posterior predicts the data points and thus this objective will tend to favour points with low predictive likelihood to be selected. That is, these points are important when *all other points* are presented, (ii) there will be *no diversity* in the memory as illustrated in the 2d classification example.\n\nThis means the points selected here have quite different characteristics compared to coreset points or the full dataset in general. Coreset points ideally can compactly represent the full dataset and can be used for inference in place of the full dataset.\n\nThat said, the contribution presented here is very useful. Just that I\u2019m not sure how well this will work in more challenging continual learning set-up where diversity is important for a long sequence of diverse tasks.\n\nb. The paper presents a post-hoc modification to approximate posterior (\u2018s mean and variance) to account for the decay in the likelihood contribution from past data. I wonder if this post-hoc adjustment is identical to running approximate inference with the adjusted likelihood?\n\nc. the adaptation parameters need to be tuned/known in advance, which seems to be a disadvantage of the current approach. Is the update presented here somewhat robust to mis-specification of these parameters? How would these be tuned in a more practical setting in which data arrive sequentially and we might not know the underlying \u201cconcept\u201d.\n\n6: Weak Accept", "turbo-16k-0613_pred": "Title: A Novel Approach for Online Learning and Adaptation with Bayesian Neural Networks\n\nReview:\nThe paper presents a novel approach for online learning and adaptation with Bayesian neural networks (BNNs). The authors propose a method called Gaussian Residual Scoring (GRS) that combines a running memory with a Gaussian approximation to learn from non-stationary streaming data. The memory update score function selects data close to the decision boundary, and the adaptation methods based on Bayesian exponential forgetting and diffusion processes allow the model to adapt to changing data distributions.\n\nThe experimental results demonstrate the superiority of GRS compared to existing memory-based online inference methods. The approach achieves better predictive performance and avoids catastrophic forgetting, making it suitable for non-stationary data. The authors also provide insights into the behavior of online VB with BNNs, highlighting the tendency for poor local minima and the challenges of training BNNs with small datasets.\n\nSuggestions:\n1. The paper can benefit from a more detailed discussion on the limitations of the proposed approach. For example, how does GRS perform under different levels of concept drift or with high-dimensional datasets?\n\n2. It would be interesting to see how GRS compares to other state-of-the-art methods for online learning and adaptation, such as continual learning algorithms or deep reinforcement learning algorithms, to provide a broader perspective on the performance of the proposed approach.\n\nQuestions:\n1. How scalable is GRS to large-scale datasets and complex models, considering the relatively high computational complexity of the method?\n\n2. Are there any additional hyperparameters in GRS besides the memory size and adaptation parameters? How sensitive is the performance to these hyperparameter choices?\n\nIn summary, the paper introduces an innovative approach that addresses the challenges of online learning and adaptation with BNNs. The experimental results demonstrate the effectiveness of the proposed method and contribute to the field of continual and lifelong learning. However, further experiments and discussions on the limitations and scalability of the approach would enhance the paper's contribution.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency \u2014 a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN. Such networks, however, are not very often applied to real life tasks. In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency. Our theoretical results are verified by a series of extensive computational experiments.", "turbo-16k-0613_pred": "This paper presents a theoretical analysis of Recurrent Neural Networks (RNNs) with different nonlinearities and their expressive power. The authors focus on generalized tensor networks, which include tensor decompositions and feature tensors. They show that generalized RNNs with rectifier nonlinearities are universal and can represent any tensor of any order and size. However, when comparing their expressive power to generalized shallow networks, they find that RNNs with rectifier nonlinearities are only exponentially more efficient for certain functions. This means that shallow networks with exponentially large width are required to realize the same functions as these deep RNNs. The authors also provide experiments on computer vision and natural language processing datasets to validate their theoretical findings. They conclude that while RNNs with rectifier nonlinearities can outperform shallow networks in certain cases, there exists a subset of RNNs for which the equivalent shallow networks are more efficient.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: On the expressive power of deep learning: A tensor analysis , Authors: Nadav Cohen Or Sharir... , 2016\n2.  Title: Boosting dilated convolutional networks with mixed tensor decompositions , Authors: Nadav Cohen Ronen Tamari... , 2018\n3.  Title: Hierarchical singular value decomposition of tensors , Authors: Lars Grasedyck , 2029\n4.  Title: Foundations of the PARAFAC procedure: Models and conditions for an \u201dexplanatory\u201d multimodal factor analysis , Authors: Richard A Harshman , 1970\n5.  Title: Expressive power of recurrent neural networks , Authors: Valentin Khrulkov Alexander Novikov... , 2018\n6.  Title: Speedingup convolutional neural networks using fine-tuned cp-decomposition , Authors: Vadim Lebedev Yaroslav Ganin... , 2015\n7.  Title: Tensorizing neural networks , Authors: Alexander Novikov Dmitrii Podoprikhin... , 2015\n8.  Title: Tensor-train decomposition , Authors: Ivan V Oseledets , 2011\n9.  Title: On multiplicative integration with recurrent neural networks , Authors: Yuhuai Wu Saizheng Zhang... , 2016\n10.  Title: Tensor-train recurrent neural networks for video classification , Authors: Yinchong Yang Denis Krompass... , 2017\n11.  Title: Long-term forecasting using tensor-train RNNs , Authors: Rose Yu Stephan Zheng... , 2017\n", "gt": "Tensor methods have a rich history of successful application in machine learning. (Vasilescu & Terzopoulos, 2002), in their framework of TensorFaces, proposed to treat facial image data as multidimensional arrays and analyze them with tensor decompositions, which led to significant boost in face recognition accuracy. (Bailey & Aeron, 2017) employed higher-order co-occurence data and tensor factorization techniques to improve on word embeddings models. Tensor methods also allow to produce more accurate and robust recommender systems by taking into account a multifaceted nature of real environments (Frolov & Oseledets, 2017). In recent years a great deal of work was done in applications of tensor calculus to both theoretical and practical aspects of deep learning algorithms. (Lebedev et al., 2015) represented filters in a convolutional network with CP decomposition (Harshman, 1970; Carroll & Chang, 1970) which allowed for much faster inference at the cost of a negligible drop in performance. (Novikov et al., 2015) proposed to use Tensor Train (TT) decomposition (Oseledets, 2011) to compress fully\u2013connected layers of large neural networks while preserving their expressive power. Later on, TT was exploited to reduce the number of parameters and improve the performance of recurrent networks in long\u2013term forecasting (Yu et al., 2017) and video classification (Yang et al., 2017) problems. In addition to the practical benefits, tensor decompositions were used to analyze theoretical aspects of deep neural nets. (Cohen et al., 2016) investigated a connection between various network architectures and tensor decompositions, which made possible to compare their expressive power. Specifically, it was shown that CP and Hierarchial Tucker (Grasedyck, 2010) decompositions correspond to shallow networks and convolutional networks respectively. Recently, this analysis was extended by (Khrulkov et al., 2018) who showed that TT decomposition can be represented as a recurrent network with multiplicative connections. This specific form of RNNs was also empirically proved to provide a substantial performance boost over standard RNN models (Wu et al., 2016). First results on the connection between tensor decompositions and neural networks were obtained for rather simple architectures, however, later on, they were extended in order to analyze more practical deep neural nets. It was shown that theoretical results can be generalized to a large class of CNNs with ReLU nonlinearities (Cohen & Shashua, 2016) and dilated convolutions (Cohen et al., 2018), providing valuable insights on how they can be improved.", "turbo-16k-0613_pred": "In recent years, Recurrent Neural Networks (RNNs) have gained popularity as a powerful deep learning technique for processing sequential data such as text, audio, or video. They are known for their ability to efficiently represent complex functions with a small number of parameters, referred to as depth efficiency. However, the underlying theory behind this phenomenon is not fully understood.\n\nA recent line of work has focused on comparing different deep learning architectures in terms of their expressive power. It has been shown that Convolutional Neural Networks (ConvNets) with product pooling and RNNs with multiplicative recurrent cells are exponentially more expressive than shallow networks. However, the analysis of RNNs with rectifier nonlinearities, which are commonly used in practice, poses a challenge as the tools used for analyzing other architectures cannot be applied in this case.\n\nTo address this challenge, the authors propose the use of generalized tensor decompositions and extend the analysis to RNNs with rectifier nonlinearities. They show that these RNNs exhibit particular recurrent structures similar to that of standard RNNs. Furthermore, they prove the universality of generalized RNNs and the existence of depth efficiency in such networks.\n\nIn addition to the theoretical analysis, the authors also conduct experiments to support their findings. They evaluate the performance of different architectures on various datasets, including MNIST, CIFAR-10, and IMDB. The results show that the choice of nonlinearity can have a significant impact on the performance of the networks, highlighting the importance of understanding the expressive power of different architectures.\n\nOverall, this study provides insights into the expressive power of RNNs with rectifier nonlinearities and highlights the potential of generalized tensor decompositions in deep learning. The findings contribute to the theoretical understanding of RNNs and have implications for practical applications in natural language processing, computer vision, and other domains.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors extend the theoretical results of a paper previously presented in the last edition of ICLR (2018), where it was demonstrated that Recurrent Neural Network can be interpreted as a tensor network decomposition based on the Tensor-Train (TT, Oseledets et al, 2011).\n\nThe paper is well written and have a good structure. However, I found that some deep concepts are not well introduced, and maybe other more trivial results are discussed with unnecessary details. The following comments could help authors to improve the quality of presentation of their paper:\n-\tSection 3.1 (Score Functions and Feature Tensor) is a bit short and difficult to read. \no\tMaybe, a more motivating introduction could be included in order to justify the definition of score functions (eq. 2). \no\tIt would be also nice to state that, according to eq. (3), the feature tensor is a rank-1 tensor. \no\tI would suggest moving the definition of outer product to the Appendix, since most readers know it very well.\no\tIt is said that eq. 2 possesses the universal approximation property (it can approximate any function with any prescribed precision given sufficiently large M). It is not clear which is the approximation function.\n-       When scores functions are presented (eq. 2) it is written the term \"logits\" between brackets. Could you please clarify why this term is introduced here? Usually, logit of a probability p is defined as L(p)=p/(1-p). What is the usage of this term in this work? \n-      I think the theory is presented for a model with the two-classes only but used for multiple classes in the experimental sections. It should be necessary to make some comment about this in the paper.\n-      Details about how the RNN based on TT is applied must be added. More specifically, the authors should provide answers to clarify the following questions: \n(i) Are patches overlapped or non-overlapped? \n(ii) What value of M is used? and is there any general rule for this choice? \n(iii) How the classification in the 10-classes is obtained? Are you using a softmax function in the last layer? Are you using one weight tensor W_c per class (c=1,2,...,10). Please provide these technical details. \n(iv) Please, specify which nonlinear activation sigma is used in the feature map f_\\theta(x).\n(v) How many feature maps are used? and, Are the matrix A and vector b learned from training dataset or only the TT-cores need to be learned? \n7: Good paper, accept", "turbo-16k-0613_pred": "Title: Review of \"Generalized Tensor Networks for Recurrent Neural Networks\"\n\nSummary:\nThe paper \"Generalized Tensor Networks for Recurrent Neural Networks\" presents a theoretical analysis of the relationship between Recurrent Neural Networks (RNNs) and Tensor Train (TT) decomposition. It introduces generalized tensor networks that incorporate various nonlinearities in RNN architectures, with a focus on the rectifier nonlinearity. The paper explores the universality and expressive power of these networks. Experimental results on computer vision and natural language processing tasks confirm the theoretical findings, showing the effectiveness of generalized RNNs compared to shallow networks. The paper also discusses the limitations of expressivity in generalized RNNs with rectifier nonlinearities.\n\nReview:\nThe paper provides a comprehensive analysis of generalized tensor networks for RNNs, highlighting their theoretical and practical implications. The results are significant as they shed light on the architecture and expressive power of RNNs with different nonlinearities. The theoretical analysis is well-supported by empirical experiments on various datasets.\n\nSuggestions:\n1. The paper could benefit from providing more insights into the interpretability and generalization performance of generalized RNNs compared to standard RNN architectures. This would enhance the understanding of the practical implications of the proposed models.\n\n2. It would be interesting to see how these generalized tensor networks perform on more complex tasks, such as machine translation or speech recognition. An investigation into the scalability and efficiency of these networks in handling larger datasets would also be valuable.\n\nQuestions:\n1. Do the proposed generalized tensor networks have any advantages over standard RNN architectures in terms of training time, memory requirements, or convergence properties?\n\n2. How do these generalized tensor networks compare to other popular RNN architectures, such as LSTMs or GRUs, in terms of performance and complexity?\n\nIn conclusion, the paper provides valuable theoretical insights and empirical evidence on the effectiveness of generalized tensor networks for RNNs. The experimental results demonstrate the potential of these architectures for solving sequential data processing tasks. Further research in this area could lead to novel approaches for improving the performance of RNNs on various applications.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Simultaneous machine translation models start generating a target sequence before they have encoded the source sequence. Recent approaches for this task either apply a fixed policy on a state-of-the art Transformer model, or a learnable monotonic attention on a weaker recurrent neural network-based structure. In this paper, we propose a new attention mechanism, Monotonic Multihead Attention (MMA), which extends the monotonic attention mechanism to multihead attention. We also introduce two novel and interpretable approaches for latency control that are specifically designed for multiple attention heads. We apply MMA to the simultaneous machine translation task and demonstrate better latency-quality tradeoffs compared to MILk, the previous state-of-the-art approach. We analyze how the latency controls affect the attention span and we study the relationship between the speed of a head and the layer it belongs to. Finally, we motivate the introduction of our model by analyzing the effect of the number of decoder layers and heads on quality and latency.", "turbo-16k-0613_pred": "In this paper, the authors propose two variants of the monotonic multihead attention model for simultaneous machine translation. They introduce the concept of monotonic multihead attention (MMA) which combines the expressive power of multihead attention with the low latency of monotonic attention. They propose two variations of MMA, namely Hard MMA (MMA-H) and Infinite Lookback MMA (MMA-IL), each designed to address different translation requirements. They also propose two latency regularization methods to control the latency of the model. The authors conduct experiments on two standard machine translation datasets and evaluate the quality and latency of their models. The results show that MMA models achieve better latency-quality tradeoffs compared to previous state-of-the-art models. The authors also analyze the attention span, the effect of the number of layers and heads on quality and latency, and the behavior of attention heads in MMA models. Overall, the proposed MMA models provide a strong foundation for simultaneous machine translation and have the potential to be applied in real-time interpretation and other language-related applications.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Incremental decoding and training methods for simultaneous translation in neural machine translation , Authors: Fahim Dalvi Nadir Durrani... , 2018\n2.  Title: Don\u2019t until the final verb wait: Reinforcement learning for simultaneous machine translation , Authors: Alvin Grissom II He He... , 2014\n3.  Title: Learning to translate in real-time with neural machine translation , Authors: Jiatao Gu Graham Neubig... , 2017\n4.  Title: Learning hard alignments with variational inference , Authors: Dieterich Lawson Chung-Cheng Chiu... , 2018\n5.  Title: Learning online alignments with continuous rewards policy gradient , Authors: Yuping Luo Chung-Cheng Chiu... , 2017\n6.  Title: STACL: Simultaneous translation with implicit anticipation and controllable latency using prefix-to-prefix framework , Authors: Mingbo Ma Liang Huang... , 2019\n", "gt": "Recent work on simultaneous machine translation falls into three categories. In the first one, models use a rule-based policy for reading input and writing output. Cho & Esipova (2016) propose a WaitIf-* policy to enable an offline model to decode simultaneously. Ma et al. (2019) propose a wait-k policy where the model first reads k tokens, then alternates between read and write actions. Dalvi et al. (2018) propose an incremental decoding method, also based on a rule-based schedule. In the second category, a flexible policy is learnt from data. Grissom II et al. (2014) introduce a Markov chain to phrase-based machine translation models for simultaneous machine translation, in which they apply reinforcement learning to learn the read-write policy based on states. Gu et al. (2017) introduce an agent which learns to make decisions on when to translate from the interaction with a pre-trained offline neural machine translation model. Luo et al. (2017) used continuous rewards policy gradient for online alignments for speech recognition. Lawson et al.", "turbo-16k-0613_pred": "Related Work\n\nSeveral previous works have focused on the topic of simultaneous machine translation, which involves generating translations in real time. Dalvi et al. (2018) proposed incremental decoding and training methods for simultaneous translation in neural machine translation. They introduced a word-by-word prediction approach to improve translation quality and latency. Grissom et al. (2014) presented a reinforcement learning framework for simultaneous machine translation, where the model learns to generate translations incrementally. Gu et al. (2017) explored the use of neural machine translation for real-time translation, introducing techniques to handle latency and ensure smooth and continuous translation output. \n\nIn the context of attention mechanisms, Chiu et al. (2018) introduced a model that learns hard alignments with variational inference. By incorporating a hard attention mechanism, the model is able to generate translations incrementally. Luo et al. (2017) proposed a continuous rewards policy gradient method for learning online alignments. This method allows the model to dynamically adjust attention during translation in order to achieve better trade-offs between quality and latency. \n\nMore recently, Ma et al. (2019) presented STACL, a framework for simultaneous translation with implicit anticipation and controllable latency. STACL uses a prefix-to-prefix framework to enable efficient and flexible translation with adjustable latency. The authors demonstrate improved translation quality and latency control compared to previous approaches.\n\nThese works have made significant contributions to the field of simultaneous machine translation, providing insights and techniques for handling real-time translation with improved quality and latency.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper proposes a fully transformer-based monotonic attention framework that extends the idea of MILK. Though the idea of monotonic multi-head attention sounds interesting, I still have some questions below:\n\nAbout the method:\n   1. Is that possible that the MMA would have worse latency than MILK since all the attention heads need to agree to write while MILK only has one attention head?\n   2. Is there any attention order between different attention head?\n   3. I think the MMA only could control the latency during training time, which would produce different models with different latency. Is there any way that enables MMA to control the latency during inference time? Can we change the latency for on given model by tuning the requirements mentioned in (1)?\n\nAbout the experiments:\n    1. Do you have any explanation of why both MMA-H and MMA-IL have better BLEU when AL is small? The results in fig 2 seem counterintuitive. \n    2. I suggest the authors do more analysis of the difference between different attention heads to prove the effectiveness of MMA. \n    3. For the left two figures in fig 4, which one is the baseline, and which one is the proposed model?\n\nI also suggest the authors present more real sample analysis and discussions about the experiments.\n6: Weak Accept", "turbo-16k-0613_pred": "Review:\n\nIn the paper \"Monotonic Multihead Attention for Simultaneous Machine Translation,\" the authors propose two variants of the monotonic multihead attention model for simultaneous machine translation. The goal of their research is to achieve better quality-latency trade-offs compared to previous models. \n\nThe paper starts by introducing simultaneous machine translation, which combines machine translation with real-time interpretation capabilities. The authors explain the traditional translation model, where the encoder first reads the entire sentence, and the decoder writes the target sentence. In contrast, simultaneous models start generating a translation before finishing reading the entire source sentence.\n\nThe authors then discuss monotonic attention mechanisms, including hard monotonic attention, monotonic chunkwise attention, and monotonic infinite lookback attention. These mechanisms provide ways to control the alignment between source and target tokens and improve latency-quality trade-offs. However, these models were based on RNN-based models, which have been outperformed by the Transformer model.\n\nTo address this, the authors propose monotonic multihead attention (MMA), which combines the high translation quality of multilayer multihead attention with the low latency of monotonic attention. They introduce two variants of MMA: MMA-H and MMA-IL. MMA-H is designed for streaming systems with limited attention span, while MMA-IL prioritizes translation system quality.\n\nThe paper presents the main contributions, including the novel monotonic multihead attention mechanism, better latency/quality trade-offs compared to previous state-of-the-art models, and analyses of attention span and speed of heads in the model. \n\nOverall, the paper provides a clear and detailed explanation of the proposed models and their advantages over previous approaches. The experiments and analyses conducted by the authors demonstrate the effectiveness of the MMA models in achieving better quality-latency trade-offs. The paper could benefit from further exploration and discussion of the limitations of the proposed models, such as the threshold of reading action and the impact of model hyperparameters on performance. Additionally, it would be interesting to see how the proposed models compare to other recently developed models in simultaneous machine translation.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Robustness is an important property to guarantee the security of machine learning models. It has recently been demonstrated that strong robustness certificates can be obtained on ensemble classifiers generated by input randomization. However, tight robustness certificates are only known for symmetric norms including `0 and `2, while for asymmetric norms like `1, the existing techniques do not apply. By converting the likelihood ratio into a one dimensional mixed random variable, we derive the first tight `1 robustness certificate under isotropic Laplace distributions in binary case. Empirically, the deep networks smoothed by Laplace distributions yield the state-of-the-art certified robustness in `1 norm on CIFAR-10 and ImageNet.", "turbo-16k-0613_pred": "This paper presents a novel approach for certifying the robustness of deep learning models against adversarial attacks. The authors focus on the `1 norm, which is asymmetric and poses challenges for existing certification techniques. They propose a tight `1 robustness guarantee under isotropic Laplace distributions, using a combination of the inequality from differential privacy and the Neyman-Pearson Lemma. The derived certification algorithm can be applied to binary classification problems and yields state-of-the-art certified accuracy in large-scale experiments on CIFAR-10 and ImageNet datasets. The method outperforms previous approaches that use Gaussian distributions for `2 norm certification. The authors also demonstrate that Laplace noise provides better protection for the original predictions compared to Gaussian noise. Theoretical analysis and empirical results support the effectiveness of the proposed approach in generating robustness certificates for deep learning models. Overall, this work contributes to the field of adversarial robustness by offering a novel method for certifying models against attacks in the `1 norm using Laplace distributions.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Sorting out lipschitz function approximation , Authors: Cem Anil James Lucas... , 2018\n2.  Title: A unified view of piecewise linear neural network verification , Authors: Rudy Bunel Ilker Turkaslan... , 2018\n3.  Title: Maximum resilience of artificial neural networks , Authors: Chih-Hong Cheng Georg N\u00fchrenberg... , 2017\n4.  Title: Parseval networks: Improving robustness to adversarial examples , Authors: Moustapha Cisse Piotr Bojanowski... , 2017\n5.  Title: Output range analysis for deep neural networks , Authors: Souradeep Dutta Susmit Jha... , 2017\n6.  Title: Regularisation of neural networks by enforcing lipschitz continuity , Authors: Henry Gouk Eibe Frank... , 2018\n7.  Title: On the effectiveness of interval bound propagation for training verifiably robust models , Authors: Sven Gowal Krishnamurthy Dvijotham... , 2018\n8.  Title: Differentiable abstract interpretation for provably robust neural networks , Authors: Matthew Mirman Timon Gehr... , 2018\n9.  Title: Fast and effective robustness certification , Authors: Gagandeep Singh Timon Gehr... , 2018\n10.  Title: Evaluating robustness of neural networks with mixed integer programming , Authors: Vincent Tjeng Kai Y Xiao... , 2017\n11.  Title: Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks , Authors: Yusuke Tsuzuku Issei Sato... , 2018\n12.  Title: Enresnet: Resnet ensemble via the feynman-kac , Authors: Bao Wang Binjie Yuan... , 2018\n13.  Title: Towards fast computation of certified robustness for relu networks , Authors: Tsuiwei Weng Huan Zhang... , 2018\n14.  Title: Efficient neural network robustness certification with general activation functions , Authors: Huan Zhang Tsui-Wei Weng... , 2018\n", "gt": "Robustness of a model can be defined in various aspects. For example, Feynman-Kac Formalism can be used to improve robustness (Wang et al., 2018). In this paper, we focus on the classification setting, where the goal is to provide guarantee of a constant prediction among a small region specified via some metric. The robustness certificate can be either exact or conservative, so long as a constant prediction is guaranteed in the certified region. Note that the certification of a completely black-box model requires checking the prediction values at every point around the point of interest, which is clearly infeasible. A practical certification algorithm inevitably has to specify and leverage the functional structure of the classifier in use to reduce the required computation. Exact certificates. The exact certificate of deep networks is typically derived for the networks with a piecewise linear activation function such as ReLU. Such networks have an equivalent mixed integer linear representation (Cheng et al., 2017; Lomuscio & Maganti, 2017; Dutta et al., 2017; Bunel et al., 2018). Hence, one may apply mixed integer linear programming to find the worst case adversary within any convex polyhedron such as an `1-ball or `\u221e-ball. Despite the elegant solution, the complexity is, in general, NP-hard and the algorithms are not scalable to large problems(Tjeng et al., 2017). Conservative certificates. A conservative certificate can be more scalable than the exact methods, since one can trade-off the accuracy of certification with efficiency (Gouk et al., 2018; Tsuzuku et al., 2018; Cisse et al., 2017; Anil et al., 2018; Hein & Andriushchenko, 2017). For example, one can relax the search of the worst case adversary as a simpler optimization problem that only bounds the effect of such adversary. Alternatively, people also consider the robustness problem in a modular way, where the robustness guarantee can be derived iteratively for each layer in the deep networks by considering the feasible values for each hidden layer (Gowal et al., 2018; Weng et al., 2018; Zhang et al., 2018; Mirman et al., 2018; Singh et al., 2018).", "turbo-16k-0613_pred": "Related Work\n\nThere have been many studies focusing on robustness in neural networks. Anil et al. (2018) examined the approximation of Lipschitz functions, while Bunel et al. (2018) proposed a unified view of piecewise linear neural network verification. Cheng et al. (2017) explored the maximum resilience of artificial neural networks, and Cisse et al. (2017) introduced Parseval networks to improve robustness against adversarial examples. Dutta et al. (2017) conducted output range analysis for deep neural networks, and Gouk et al. (2018) investigated the regularization of neural networks by enforcing Lipschitz continuity.\n\nOther works focused on certification of robustness in neural networks. Gowal et al. (2018) evaluated the effectiveness of interval bound propagation, and Mirman et al. (2018) proposed differentiable abstract interpretation for provably robust networks. Singh et al. (2018) developed fast and effective robustness certification methods, and Tjeng et al. (2017) used mixed integer programming to evaluate neural network robustness. Tsuzuku et al. (2018) introduced Lipschitz-margin training for perturbation invariance, while Wang et al. (2018) proposed ENResNet, a ResNet ensemble based on Feynman-Kac. Weng et al. (2018) developed an efficient computation method for certified robustness, and Zhang et al. (2018) focused on efficient certification with general activation functions.\n\nThese studies provide valuable insights and techniques for enhancing the robustness of neural networks and certifying their performance in the face of adversarial attacks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "In this paper, the author derived a tight ell_1, which is not the symmetric norm, robustness certificates under isotropic Laplace distributions. Experimentally, the authors showed that the deep networks smoothed \nby Laplace distributions yield the state-of-the-art certified robustness in ell_1 norm on the CIFAR-10 \nand ImageNet. To find the ell_1 certificate, the authors first identified the tight robustness certificate, for attacking the model in one particular direction, say the first direction. To show that any other perturbation directions cannot lead to a worse result, the authors convert the d dimensional likelihood function into a one-dimensional function, and the authors used relaxation for different perturbations and show that the worst-case result is bounded by the previously identified direction.  However, I have the following concerns about this work:\n\n1. Theoretically, the authors only showed the certificate is tight for binary classification. I would suggest\nthe author change their claim in the abstract.\n\n2. What is M on page 3 which is used without definition after definition 1?\n\n3. Can you give a concrete continuous probability distribution that leads to the scenario in Fig.~3?\n\n4. Can you extend the analysis to a multi-class classification scenario?\n\n5. Besides randomized smoothing on the input images, recently Wang et al showed that randomize the deep nets can\nalso improve the deep nets and they gave it a nice theoretical interpretation. Here is the reference: Bao Wang, Binjie Yuan, Zuoqiang Shi, Stanley J. Osher. ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies, arXiv:1811.10745, NeurIPS, 2019\n\nOverall, since this work is a straightforward integration of some existing work, I think this\npaper lack novelty. Please address the above questions in rebuttal.\n3: Weak Reject", "turbo-16k-0613_pred": "Review:\n\nIn this paper, the authors propose a novel approach for certifying the robustness of deep learning models against adversarial attacks. They focus on the `1 norm and derive a tight `1 robustness guarantee under isotropic Laplace distributions. The authors present theoretical results, proving the effectiveness of their approach in both binary and multi-class classification settings. They use the randomized smoothing technique to obtain robustness certificates and demonstrate superior empirical results on CIFAR-10 and ImageNet datasets with ResNet models.\n\nThe paper is well-written and presents a valuable contribution to the field of adversarial robustness. The authors provide a clear explanation of their methodology and provide detailed proofs for their theorems. The experimental results show that their approach outperforms existing methods, achieving higher certified accuracy.\n\nHowever, there are a few points that could be improved in the paper. Firstly, it would be helpful to provide a more detailed comparison with related work. While the authors mentioned existing approaches, more in-depth analysis and comparison could further strengthen their argument. Additionally, it would be beneficial to see more discussion on the limitations and potential future directions of their proposed method. How does their approach scale to larger datasets or more complex models?\n\nOverall, this paper presents a promising approach for certifying the robustness of deep learning models. The theoretical proofs and the empirical results support the effectiveness of their approach. With some minor improvements, such as a more comprehensive comparison with related work and discussion on limitations, the paper could make an even stronger impact in the field.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., L2 regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment. In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks. Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvements on the task performance, and the improvement is typically more significant when the task is more difficult. We also compare with the widely used entropy regularization and find L2 regularization is generally better. Our findings are further confirmed to be robust against the choice of training hyperparameters. We also study the effects of regularizing different components and find that only regularizing the policy network is typically the best option. We hope our study provides guidance for future practices in regularizing policy optimization algorithms.", "turbo-16k-0613_pred": "Regularization techniques, such as L2 regularization and dropout, are widely used in supervised learning tasks to prevent overfitting in neural networks. However, in deep reinforcement learning (RL), these conventional regularization methods have been largely absent or underutilized. In this study, the authors aim to question the effectiveness of not using common regularizations in RL and evaluate the performance of popular policy optimization algorithms, such as A2C, TRPO, PPO, and SAC, with different regularization techniques. They find that even though the training and testing environments are the same, many conventional regularization methods can significantly improve performance in RL, especially on harder tasks. L2 regularization tends to be the most effective, outperforming entropy regularization. L1 regularization and weight clipping also show improvements in certain cases. Batch Normalization and dropout are more effective in off-policy algorithms. The authors conclude that regularizations can be as important as high-level RL algorithms in boosting performance and suggest regularizing the policy network alone as the best practice. Overall, their comprehensive study sheds light on the importance of regularization methods in policy optimization algorithms and their potential for improving RL performance.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Control regularization for reduced variance reinforcement learning , Authors: Richard Cheng Abhinav Verma... , 1905\n2.  Title: Addressing function approximation error in actorcritic methods , Authors: Scott Fujimoto Herke van Hoof... , 2018\n3.  Title: Imformation asymmetry in kl-regularized rl , Authors: Alexandre Galashov Siddhant Jayakumar... , 2019\n4.  Title: Continuous control with deep reinforcement learning , Authors: Timothy Lillicrap Jonathan Hunt... , 2016\n5.  Title: Continuous control with deep reinforcement learning , Authors: Timothy P Lillicrap Jonathan J Hunt... , 2015\n6.  Title: Td-regularized actor-critic methods , Authors: Simone Parisi Voot Tangkaratt... , 2019\n7.  Title: Trust region policy optimization , Authors: John Schulman Sergey Levine... , 2015\n8.  Title: High-dimensional continuous control using generalized advantage estimation , Authors: John Schulman Philipp Moritz... , 2016\n9.  Title: Proximal policy optimization algorithms , Authors: John Schulman Filip Wolski... , 2017\n", "gt": "Regularization in Deep RL. Conventional regularization methods have rarely been applied in deep RL. One rare case of such use is in Deep Deterministic Policy Gradient (DDPG) (Lillicrap et al., 2016), where Batch Normalization is applied to all layers of the actor \u00b5 network and layers of the critic Q network prior to the action input, and L2 regularization is applied to the critic Q network because it tends to have overestimation bias (Fujimoto et al., 2018). Some recent studies have developed more complicated regularization approaches to continuous control tasks. Cheng et al. (2019) regularizes the stochastic action distribution \u03c0(a|s) using a suboptimal control prior. The regularization weight at a given state is adjusted based on the temporal difference (TD) error. The larger the TD error, the more the action distribution moves towards the prior. Galashov et al. (2019) introduces a default policy that receives limited information as a regularizer. The information asymmetry between the behavior policy and the default policy helps to accelerate convergence and improve performance. Parisi et al. (2019) introduces TD error regularization to penalize inaccurate value estimation and Generalized Advantage Estimation (GAE) (Schulman et al., 2016) regularization to penalize GAE variance. However, most of these regularizations are rather complicated (Galashov et al., 2019), specifically designed for certain algorithms (Parisi et al., 2019), or need prior information (Cheng et al., 2019). Also, these techniques consider regularizing the output\nof the network, while conventional regularization methods mostly directly regularize the parameters.", "turbo-16k-0613_pred": "The paper discusses the importance of regularization in neural network training, particularly in the context of deep reinforcement learning (RL). It highlights that while regularization methods like L2 regularization and dropout are widely used in supervised learning tasks, they are largely absent in deep RL research. The paper argues that there is a lack of generalization gap in deep RL because the training and testing environments are usually the same. However, the authors take an empirical approach to question the conventional wisdom and study the impact of regularization methods on policy optimization methods in deep RL.\n\nThe authors conduct experiments using four popular policy optimization algorithms (SAC, PPO, TRPO, A2C) on multiple continuous control tasks. They compare the performance of different regularization techniques (L2/L1 weight regularization, weight clipping, dropout, batch normalization) to that without regularization and to entropy regularization, which is commonly used in RL. Surprisingly, they find that many conventional regularization techniques can still significantly improve performance, with L2 regularization being the most effective in most cases. They also find that regularization methods are more effective on harder tasks and that regularizing the policy network alone is usually sufficient for improving performance.\n\nIn conclusion, the paper provides a comprehensive study of regularization methods in policy optimization algorithms for deep RL, which have been largely ignored in previous research. It demonstrates the effectiveness of conventional regularization techniques in improving performance on continuous control tasks and highlights the importance of considering network training techniques in addition to high-level reinforcement learning algorithms.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors present that regularization methods mostly help to improve the agents' performance in terms of final scores. Specifically, they show that direct regularizations on model parameters, such as the standard case of L2 or L1 regularization, generally improve the agent performance. They also show that these regularizations, in their study, is more proper than entropy regularization. The authors also show that, in the presence of such regularizations, the learning algorithms become less sensitive to the hyperparameters. \n\nFew comments:\n1) The paper is well written and easy to follow. I appreciate it. I found the writing of the paper has a bit of repetition. The authors might find it slightly more proper to remove some of the repetitions (e.g. section 4.2)\n2) I would suggest rephrasing the last two sentences of the second paragraph in related work: \"Also, these techniques consider ...\". Regularizing the output also regularizes the parameters, I think the authors' point was \"directly regularize\" the parameters. \n3) In the \"Entropy Regularization\" part of section 3, I guess the Hs has not been defined. \n4) Repeated \"the\" in the last paragraph of section 4.1 (despite it already incorporates the the maximization of)\n5) The authors used the term \"not converge\" multiple times. While it is hard from the plots to see whether the series converges or not, I have a strong feeling that by this term the authors mean the algorithm does not converge to a resealable solution rather than being divergent up to a bandwidth. Maybe clarifying would be helpful.\n6) In section 5, the authors study the sensitivity to the hyperparameters. In this section, I had a hard time to understand the role of term 3\n\"BN and dropout hurts on-policy algorithms but can bring improvement only for the off-policy SAC algorithm.\" Does it mean that deploying BN, results in a more sensitive algorithm? or it means that the performance degrades (which is a different topic than section 5 is supposed to serve)?\n7) In section 7, the authors put out a hypothesis \"\nHowever, there is still generalization between samples: the agents are only trained on the limited\" but the provided empirical study might not fully be considered to be designed to test this hypothesis. In order to test this hypothesis, the author might be interested in training the models with bigger sample sizes, more training iteration, different function classes, and more fitting in order to test this hypothesis.\n\n3: Weak Reject", "turbo-16k-0613_pred": "The paper provides a comprehensive study on the application of regularization techniques in policy optimization algorithms for reinforcement learning. The authors investigate the effectiveness of various regularization methods, such as L2/L1 weight regularization, weight clipping, dropout, and batch normalization, in improving the performance of different algorithms on continuous control tasks.\n\nOne of the key findings is that conventional regularization methods, particularly L2 regularization, are effective in improving performance, even more so than the widely used entropy regularization. The results also demonstrate that the effectiveness of regularization methods depends on the algorithm and task difficulty. L2 regularization tends to be the most effective overall, while L1 regularization and weight clipping show promising results in certain cases. Dropout and batch normalization are found to be helpful mainly for off-policy algorithms.\n\nThe paper also explores the impact of regularizing the policy network alone versus both the policy and value networks. The experiments show that regularizing only the policy network tends to be the most effective approach, with minimal improvement observed when regularizing the value network alone or both networks.\n\nOverall, this study sheds light on the importance of regularization techniques in deep reinforcement learning and provides valuable insights into the effectiveness of different regularization methods. The findings suggest that incorporating proper regularization can significantly enhance the performance of policy optimization algorithms. One potential direction for future research could be exploring the combination of different regularization techniques to further improve performance. Additionally, it would be interesting to investigate the generalization ability of regularized models across different environments and evaluate the impact of regularization on sample efficiency.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently. Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks. Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts. In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks. The proposed model can effectively capture both local structures and global longterm dependencies in sequences without any use of position embeddings. We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks. We have made the code and data publicly available 1.", "turbo-16k-0613_pred": "In this paper, a novel sequence learning model called R-Transformer is proposed. The R-Transformer combines the advantages of Recurrent Neural Networks (RNNs) and multi-head attention while avoiding their respective drawbacks. It consists of a LocalRNN component that captures local structures and a multi-head attention pooling component that captures long-term dependencies. Unlike RNNs, the LocalRNN operates on short sequences within local windows, effectively capturing sequential information and mitigating the issues of long-term dependencies and time complexity. The multi-head attention pooling allows direct connections between positions, while avoiding the limitations of position embeddings. The R-Transformer is evaluated on various sequence learning tasks, including image classification, polyphonic music modeling, and language modeling. The results show that the R-Transformer outperforms both RNN-based models and non-recurrent models such as TCN and the standard Transformer. The R-Transformer demonstrates its effectiveness in capturing both local structures and long-term dependencies, making it a powerful and versatile sequence learning model.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling , Authors: Shaojie Bai J Zico Kolter... , 2018\n2.  Title: Learning phrase representations using rnn encoder-decoder for statistical machine translation , Authors: Kyunghyun Cho Bart Van Merri\u00ebnboer... , 2014\n3.  Title: Language modeling with gated convolutional networks , Authors: Yann N Dauphin Angela Fan... , 2017\n4.  Title: A convolutional encoder model for neural machine translation , Authors: Jonas Gehring Michael Auli... , 2016\n5.  Title: Convolutional sequence to sequence learning , Authors: Jonas Gehring Michael Auli... , 2017\n6.  Title: Wavenet: A generative model for raw audio , Authors: A\u00e4ron Van Den Oord Sander Dieleman... , 2016\n7.  Title: Attention is all you need , Authors: Ashish Vaswani Noam Shazeer... , 2017\n", "gt": "Recurrent Neural Networks including its variants such LSTM (Hochreiter & Schmidhuber, 1997) and GRU (Cho et al., 2014) have long been the default choices for generic sequence modeling. A RNN sequentially processes each position in a sequence and maintains an internal hidden state to compresses information of positions that have been seen. While its design is appealing and it has been successfully applied in various tasks, several problems caused by its recursive structures including low computation efficiency and gradient exploding or vanishing make it ineffective when learning long sequences. Therefore, in recent years, a lot of efforts has been made to develop models\nwithout recursive structures and they can be roughly divided into two categories depending whether they rely on convolutions operations or not. The first category includes models that mainly built on convolution operations. For example, van den Oord et al. have designed an autoregressive WaveNet that is based on causal filters and dilated convolution to capture both global and local information in raw audios (Van Den Oord et al., 2016). Ghring et al. has successfully replace traditional RNN based encoder and decoder with convolutional ones and outperforms LSTM setup in neural machine translation tasks (Gehring et al., 2017; 2016). Moreover, researchers introduced gate mechanism into convolutions structures to model sequential dependencies in languages (Dauphin et al., 2017). Most recently, a generic architecture for sequence modeling, termed as Temporal Convolutional Networks (TCN), that combines components from previous works has been proposed in (Bai et al., 2018). Authors in (Bai et al., 2018) have systematically compared TCN with canonical recurrent networks in a wide range of tasks and TCN is able achieve better performance in most cases. Our R-transformer is motivated by works in this group in a sense that we firstly models local information and then focus on global ones. The most popular works in second category are those based on multi-head attention mechanism. The multi-head attention mechanism was firstly proposed in Vaswani et al.", "turbo-16k-0613_pred": "Related Work\nThere have been several research works focusing on sequence modeling using convolutional and recurrent networks. Bai et al. (2018) conducted an empirical evaluation of generic CNN and RNN architectures for sequence modeling tasks. They compared the performance of LSTM, GRU, TCN (Temporal Convolutional Network), and Transformer on various tasks and datasets. Their results showed that TCN outperformed LSTM and GRU in capturing long-term dependencies, while Transformer achieved comparable results to TCN. \n\nIn the field of machine translation, Cho et al. (2014) proposed a recurrent neural network (RNN) encoder-decoder model for learning phrase representations. The model utilized an RNN to encode the input sequence and another RNN to decode the encoded representation into the output sequence. The experiment demonstrated improved translation performance compared to traditional statistical machine translation systems.\n\nDauphin et al. (2017) introduced gated convolutional networks for language modeling. The model utilized 1-D convolutional layers with gated activation units to capture local dependencies in sequences. Their results showed that the gated convolutional model achieved competitive performance compared to LSTM models on language modeling tasks.\n\nGehring et al. (2016) proposed a convolutional encoder model for neural machine translation. The model utilized 1-D convolutional layers to capture local dependencies in input sequences and achieved competitive translation performance compared to traditional recurrent encoder-decoder models.\n\nGehring et al. (2017) extended the convolutional sequence to sequence model by introducing global attention mechanisms. They demonstrated that the global attention mechanism improved translation performance on both short and long sentences.\n\nOord et al. (2016) introduced WaveNet, a generative model for raw audio signals. WaveNet utilized dilated convolutions to capture long-term dependencies in audio sequences and achieved state-of-the-art performance in audio generation tasks.\n\nVaswani et al. (2017) proposed the Transformer model that replaced recurrent layers with self-attention mechanisms. The attention mechanism allowed direct connections between all positions in a sequence, enabling the model to capture global dependencies efficiently. The Transformer achieved state-of-the-art performance on machine translation and other sequence modeling tasks.\n\nIn conclusion, previous research has explored the use of both CNN and RNN architectures for sequence modeling tasks. TCN and Transformer models have demonstrated strong performance in capturing long-term dependencies in sequences, while also incorporating local structures effectively. These models have improved upon traditional recurrent architectures and have shown promise in various sequence modeling tasks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors propose an architecture that combines the practices of recurrent and feed-forward sequence models. However, I have major concerns regarding the novelty this paper, the various claims it makes, as well as its experiment setting.\n\nMajor issues/questions:\n1. The techniques proposed by this paper lack novelty. For instance, the entire section 3.2 is simply the original design of the multi-head self-attention by Vaswani et al. The major difference between R-Transformer and the original Transformer is the replacement of positional embedding with an RNN layer, but (in my opinion) the authors did not demonstrate sufficiently its effectiveness via ablative studies (see below). Moreover, some prior works have already exploited the locality structure in Transformers. For instance, [1] showed that a sparse, local Transformer can work extremely well and be very efficient (they achieved SOTA on large-scale char-level language modeling tasks).\n\n2. The experiments do not entirely convince me. \n    i) The authors use the \"same hidden size for R-Transformer and Transformer.\" But in fact, as the R-Transformer has one extra RNN/LSTM/GRU layer at every level of the network, the tests were carried out (in effect) using a larger model than the baselines. I think the authors should instead control the # of model parameters, especially since you are running only on small tasks with small-sized models.\n    ii) It is nice that the authors tested R-Transformer on a variety of tasks--- this is important. However, in no way do these number achieve the levels of the \"state-of-the-art\", which the authors claim at the end of Section 1 (e.g., [2] has better number on seqMNIST and character-level PTB, and the Transformer-XL actually achieves <55 perplexity on word-level PTB). Therefore, the numbers don't look particularly appealing to me. \n    iii) Lack of more challenging, or large-scale experiments.\n    iv) Lack of ablative study. \n\n3. The authors claim that the finite-window RNN captures local features. But doesn't that claim only applies to the first layer? Once the first layer multi-head attention mixes all input elements across the sequence, the \"local\" features fed into the second layer RNN will be, actually, **global** features? Doesn't that \"defeat\" the purpose of using a local RNN though?\n\n3: Weak Reject", "turbo-16k-0613_pred": "Review:\n\nThe paper proposes a novel sequence learning model, called R-Transformer, which combines the advantages of Recurrent Neural Networks (RNNs) and the multi-head attention mechanism. The R-Transformer addresses the limitations of traditional RNNs, such as gradient vanishing and the inability to parallelize computations, by introducing a LocalRNN to capture local structures within sequences. It also eliminates the need for position embeddings used in the standard Transformer model. The paper evaluates the R-Transformer on various sequence learning tasks, including sequence classification, polyphonic music modeling, and language modeling, and compares it with RNNs, TCN, and Transformer. The results show that the R-Transformer outperforms other models in terms of capturing long-term dependencies and modeling local structures.\n\nOverall, the paper presents a well-designed model and provides thorough experimental evaluations to validate its effectiveness. The introduction of the LocalRNN module successfully addresses the limitations of traditional RNNs and improves upon the performance of models like TCN and Transformer. The elimination of position embeddings also simplifies the model design. The paper clearly highlights the advantages of the R-Transformer and provides evidence through empirical experiments. \n\nHowever, there are some areas that could be further explored and discussed. First, the paper focuses on moderate model and dataset sizes, and it would be interesting to investigate the scalability and performance of the R-Transformer on larger models and datasets. This can give a better understanding of the model's limitations and generalizability. Additionally, while the R-Transformer is compared to other models, there is limited discussion on the trade-offs between the different approaches. It would be beneficial to analyze the advantages and disadvantages of each model in more depth. Lastly, the paper does not discuss the computational complexity of the R-Transformer in comparison to other models. Understanding the computational requirements can help researchers and practitioners choose the appropriate model for their specific tasks.\n\nIn conclusion, the R-Transformer is a promising model for sequence learning tasks, offering improvements over traditional RNNs, TCN, and Transformer. Further investigation into the scalability, trade-offs, and computational complexity would enhance the paper's contribution to the field.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning. It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral cloning cost. Unlike adversarial imitation methods, it uses a fixed reward function which is easy to optimize. We prove a regret bound for the algorithm which is linear in the time horizon multiplied by a coefficient which we show to be low for certain problems on which behavioral cloning fails. We evaluate our algorithm empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning.", "turbo-16k-0613_pred": "In this paper, the authors propose a new algorithm called Disagreement-Regularized Imitation Learning (DRIL) to address the covariate shift problem in imitation learning. DRIL trains an ensemble of policies on demonstration data and uses the disagreement in their predictions as a cost, which is optimized through reinforcement learning. The algorithm combines a supervised behavioral cloning cost with an uncertainty cost, which encourages the policy to return to regions of dense coverage by the expert. The authors provide theoretical results showing that the algorithm achieves a regret bound that depends on a tradeoff parameter between the concentration of the demonstration data and the diversity of the ensemble. \nThe authors evaluated DRIL on both tabular MDPs and Atari environments, and compared it against behavioral cloning and generative adversarial imitation learning. The results showed that DRIL outperformed behavioral cloning in most cases, often matching expert performance with only a few demonstration trajectories. In the Atari environments, DRIL was able to achieve high performance across a wide range of tasks. The authors also performed experiments on continuous control tasks and found that DRIL was competitive with behavioral cloning and generative adversarial imitation learning. \nIn conclusion, DRIL is a promising approach to addressing the covariate shift problem in imitation learning. It has demonstrated strong empirical performance and has a theoretical foundation that provides a regret bound on its performance. Further research could explore the application of DRIL to other structured prediction problems and investigate its robustness in different settings.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning to search better than your teacher , Authors: Kai-Wei Chang Akshay Krishnamurthy... , 2015\n2.  Title: Hierarchical imitation and reinforcement learning , Authors: Hoang M Le Nan Jiang... , 2018\n3.  Title: Learning self-correctable policies and value functions from demonstrations with negative sampling , Authors: Yuping Luo Huazhe Xu... , 2019\n4.  Title: Overcoming exploration in reinforcement learning with demonstrations , Authors: Ashvin Nair Bob McGrew... , 2018\n5.  Title: Alvinn: An autonomous land vehicle in a neural network , Authors: Dean A. Pomerleau , 1989\n6.  Title: SQIL: imitation learning via regularized behavioral cloning , Authors: Siddharth Reddy Anca D. Dragan... , 2019\n7.  Title: A reduction of imitation learning and structured prediction to no-regret online learning , Authors: Stephane Ross Geoffrey Gordon... , 2011\n8.  Title: Deeply aggrevated: Differentiable imitation learning for sequential prediction , Authors: Wen Sun Arun Venkatraman... , 2017\n9.  Title: Improving multi-step prediction of learned time series models , Authors: Arun Venkatraman Martial Hebert... , 2015\n", "gt": "The idea of learning through imitation dates back at least to the work of (Pomerleau, 1989), who trained a neural network to imitate the steering actions of a human driver using images as input. The problem of covariate shift was already observed, as the author notes: \u201cthe network must not solely be shown examples of accurate driving, but also how to recover once a mistake has been made\u201d. This issue was formalized in the work of (Ross & Bagnell, 2010), who on one hand proved an O( T 2) regret bound, and on the other hand provided an example showing this bound is tight. The subsequent work (Ross et al., 2011) proposed the DAGGER algorithm which obtains linear regret, provided the agent can both interact with the environment, and query the expert policy. Our approach also requires environment interaction, but importantly does not need to query the expert. note is the work of (Venkatraman et al., 2015), which extended DAGGER to time series prediction problems by using the true targets as expert corrections. Imitation learning has been used within the context of modern RL to help improve sample efficiency (Chang et al., 2015; Ross & Bagnell, 2014; Sun et al., 2017; Hester et al., 2018; Le et al., 2018; Cheng & Boots, 2018) or overcome exploration (Nair et al., 2017). These settings assume the reward is known and that the policies can then be fine-tuned with reinforcement learning. In this case, covariate shift is less of an issue since it can be corrected using the reinforcement signal. The work of (Luo et al., 2019) also proposed a method to address the covariate shift problem when learning from demonstrations when the reward is known, by conservatively extrapolating the value function outside the training distribution using negative sampling. This addresses a different setting from ours, and requires generating plausible states which are off the manifold of training data, which may be challenging when the states are high dimensional such as images. The work of (Reddy et al., 2019) proposed to treat imitation learning within the Q-learning framework, setting a positive reward for all transitions inside the demonstration data and zero reward for all other transitions in the replay buffer. This rewards the agent for repeating (or returning to) the expert\u2019s transitions.", "turbo-16k-0613_pred": "Related Work\n\nSeveral works have explored imitation learning and reinforcement learning methods for training artificial agents to perform complex tasks. Chang et al. (2015) introduced a method called \"Learning to Search Better Than Your Teacher\" that uses imitation learning to improve search performance. They showed that the learned policy can outperform the expert policy on unseen test data. Le et al. (2018) proposed a hierarchical approach to imitation and reinforcement learning, where the agent learns to imitate high-level actions and then refines its behavior using reinforcement learning. Luo et al. (2019) developed a method for learning self-correctable policies and value functions from demonstrations with negative sampling, which addresses the issue of suboptimal behavior learned from imperfect demonstrations.\n\nNair et al. (2018) focused on overcoming exploration challenges in reinforcement learning by incorporating demonstrations. Their approach, called \"Overcoming Exploration in Reinforcement Learning with Demonstrations,\" combines the strengths of model-free and model-based methods to improve exploration efficiency. Pomerleau (1989) proposed an autonomous land vehicle called ALVINN that used neural networks for perception and control. The system learned to drive a vehicle by imitating the behavior of a human expert.\n\nReddy et al. (2019) introduced SQIL, a method for imitation learning that combines behavior cloning with regularization. They showed that SQIL outperforms existing methods in terms of sample efficiency and robustness to distribution shift. Ross et al. (2011) presented a reduction of imitation learning and structured prediction problems to no-regret online learning. Their work provided theoretical guarantees for imitation learning algorithms. Sun et al. (2017) proposed a differentiable imitation learning method, allowing for end-to-end training of sequential prediction models. They applied their approach to various domains and showed improved performance compared to traditional methods. \n\nLastly, Venkatraman et al. (2015) focused on improving the multi-step prediction of learned time series models through techniques such as Monte Carlo tree search. Their work demonstrated the benefits of using imitation learning in the context of time series modeling. \n\nThese related works provide valuable insights into the different approaches and techniques used in imitation learning and reinforcement learning, complementing the contributions of the current study.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes a simple but effective method to address the important issue of covariate shift. The method performs well empirically and has a theoretical support (although only for a tabular setting). While there are some issues (see below), this is a good paper. I vote for acceptance.  \n\n* Major comments and questions:\n- Accuracy of posterior approximation via ensemble. \nIt is unclear whether the posterior approximated from ensemble is accurate. More specifically, these ensemble policies are trained using BC loss. Under a limited amount of data (where BC fails), these policies would also fail and are inaccurate. Therefore, it should not be expected that a posterior from these inaccurate policies is accurate. Have the authors measure or analyze accuracy of these policies or that of the posterior? This important point is not mentioned or analyzed in the paper.\n\n- Alternative approaches to posterior approximation and uncertainty computation. \nThere are other approaches to obtain a posterior besides the ensemble approach, e.g., Bayesian neural networks. Such alternatives were not mentioned in the paper. Also, there are other quantities for measuring uncertainty besides the variance such as the entropy. These approaches and quantities have different pros and cons and they should be discussed in the paper.\n\n- Sample complexity in terms of environment interactions. \nThe sample complexity in terms of environment interactions is an important criterion for IL. I suggest the authors to include this criterion in the experiments. \n\n* Minor questions:\n- Why does the minibatch size is only 4 in the experiments for all methods. This is clearly too small for a reasonable training of deep networks. Is this a typo?\n\n- It is strange to not evaluate GAIL in the continuous control experiments, since GAIL was originally evaluated in these domains. I strongly suggest the authors to evaluate GAIL (and perhaps stronger methods such as VAIL (Peng et al., 2019)) in the continuous control experiments.\n\n8: Accept", "turbo-16k-0613_pred": "In the paper \"Disagreement-Regularized Imitation Learning\" by Sun et al., the authors propose a new algorithm called DRIL to address the covariate shift problem in imitation learning. The algorithm trains an ensemble of policies on demonstration data and uses the disagreement in their predictions as a cost, which is optimized through reinforcement learning (RL) together with a supervised behavioral cloning cost. The motivation is that the ensemble policies will tend to agree on the states covered by the expert, leading to low cost, but will disagree on states not covered by the expert, leading to high cost. The RL cost guides the agent back towards the expert's distribution, while the supervised cost ensures that it mimics the expert within the expert's distribution. The authors provide theoretical results showing that their algorithm obtains a regret bound that is linear in a coefficient called \u03ba, which quantifies the tradeoff between the concentration of the demonstration data and the diversity of the ensemble outside the demonstration data. \n\nOverall, this paper presents a promising approach to address the covariate shift problem in imitation learning. The algorithm achieves good performance across multiple tasks, including video games and continuous control tasks. The use of an ensemble of policies and the combination of RL and supervised learning contribute to the algorithm's ability to match or outperform other methods such as behavioral cloning and generative adversarial imitation learning. However, there are still some questions and considerations. For example, how does the performance of DRIL compare to other state-of-the-art imitation learning algorithms? Is there any limitation or assumption in the algorithm that may affect its performance in certain scenarios? Further experiments and analysis could provide more insights and validate the effectiveness of DRIL. Additionally, it would be interesting to explore the extension of DRIL to other domains or applications, such as language modeling or dialogue systems, where covariate shift is a prevalent issue.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust and efficient measures of uncertainty are crucial. While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood function, the resulting model remains oblivious to the underlying confidence of its predictions. In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target. We accomplish this by placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution. We impose priors during training such that the model is penalized when its predicted evidence is not aligned with the correct output. Thus the model estimates not only the probabilistic mean and variance of our target but also the underlying uncertainty associated with each of those parameters. We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scales to complex computer vision tasks, and is robust to adversarial input perturbations.", "turbo-16k-0613_pred": "This paper presents a novel approach for training deterministic neural networks that can estimate both aleatoric and epistemic uncertainty. The method involves learning evidential distributions and penalizing prediction errors based on the available evidence. The approach is validated on a benchmark regression task and demonstrated to provide accurate uncertainty estimates. The method is then applied to the task of depth estimation in computer vision, where it shows scalability and comparable predictive accuracy to other models. The method also effectively captures uncertainty in out-of-distribution samples and remains robust to adversarial perturbations. The results demonstrate the ability of the approach to generate precise uncertainty metrics for robust neural network deployment in safety-critical domains. Overall, this method offers a promising solution for improving the accuracy and reliability of neural network predictions by incorporating uncertainty modeling.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Mixture density networks , Authors: Christopher M Bishop , 1994\n2.  Title: Weight uncertainty in neural networks , Authors: Charles Blundell Julien Cornebise... , 2015\n3.  Title: A weakly informative default prior distribution for logistic and other regression models , Authors: Andrew Gelman Aleks Jakulin... , 2008\n4.  Title: Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper) , Authors: Andrew Gelman , 2006\n5.  Title: Variational dropout and the local reparameterization trick , Authors: Durk P Kingma Tim Salimans... , 2015\n6.  Title: Simple and scalable predictive uncertainty estimation using deep ensembles , Authors: Balaji Lakshminarayanan Alexander Pritzel... , 2017\n7.  Title: Variational dropout sparsifies deep neural networks , Authors: Dmitry Molchanov Arsenii Ashukha... , 2017\n8.  Title: Evidential deep learning to quantify classification uncertainty , Authors: Murat Sensoy Lance Kaplan... , 2018\n", "gt": "Uncertainty estimation has a long history in neural networks, from modeling probability distribution parameters over outputs (Bishop, 1994) to Bayesian deep learning (Kendall & Gal, 2017). Our work builds on this foundation and presents a scalable representation for inferring the parameters of an evidential uncertainty distribution while simultaneously learning regression tasks via MLE. In Bayesian deep learning, priors are placed over network weights and estimated using variational inference (Kingma et al., 2015). Dropout (Gal & Ghahramani, 2016; Molchanov et al., 2017) and BBB (Blundell et al., 2015) rely on multiple samples to estimate predictive variance. Ensembles (Lakshminarayanan et al., 2017) provide a tangential approach where sampling occurs over multiple trained instances. In contrast, we place uncertainty priors over the likelihood function and thus only need a single forward pass to evaluate both prediction and uncertainty. Additionally, our approach of uncertainty estimation proved to be better calibrated and capable of predicting where the model fails. A large topic of research in Bayesian inference focuses on placing prior distributions over hierarchical models to estimate uncertainty (Gelman et al., 2006; 2008). Our methodology falls under the class of evidential deep learning which models higher-order distribution priors over neural network predictions to interpret uncertainty. Prior works in this field (Sensoy et al., 2018; Malinin & Gales, 2018) have focused exclusively on modeling uncertainty in the classification domain with Dirichlet prior distributions. Our work extends this field into the broad range of regression learning tasks (e.g. depth estimation, forecasting, robotic control learning, etc.) and demonstrates generalizability to out-of-distribution test samples and complex learning problems.", "turbo-16k-0613_pred": "In recent years, uncertainty estimation in neural networks has gained significant attention due to its importance in many applications. Several approaches have been proposed to address this problem, including Mixture Density Networks (MDNs) (Bishop, 1994), Weight Uncertainty in Neural Networks (WUNNs) (Blundell et al., 2015), and variational dropout (Kingma et al., 2015). These methods aim to capture different sources of uncertainty, such as aleatoric and epistemic uncertainty. Aleatoric uncertainty relates to the inherent noise present in the data, while epistemic uncertainty is associated with model uncertainty or lack of knowledge about the data.\n\nGelman et al. (2008) and Gelman (2006) introduced informative prior distributions to handle uncertainty in regression models. Lakshminarayanan et al. (2017) proposed Deep Ensembles, a method that combines multiple models to estimate uncertainty. Molchanov et al. (2017) introduced Variational Dropout, which sparsifies neural networks and improves uncertainty estimation. Sensoy et al. (2018) presented Evidential Deep Learning, which provides a framework for quantifying classification uncertainty.\n\nIn this paper, the authors propose a novel approach for estimating uncertainty in neural networks called evidential regression. Their method aims to simultaneously learn the desired target and evaluate the evidence in support of the target to generate robust metrics of model uncertainty. They achieve this by learning evidential distributions and penalizing prediction errors that scale with the available evidence. The authors validate their approach on a benchmark regression task and demonstrate its scalability to depth estimation in computer vision.\n\nThe authors compare their method against various baselines, including deterministic regression, MC-dropout, model ensembles, and Bayes-by-Backprop. Their results show that evidential regression outperforms these baselines in terms of predictive accuracy and uncertainty estimation. The authors also analyze the calibration of uncertainty estimates and evaluate the method's performance on out-of-distribution samples and adversarial perturbations.\n\nOverall, the proposed evidential regression approach provides a means to accurately estimate uncertainty in neural networks, which is crucial for robust model deployment in safety-critical domains. The method integrates well-established techniques from Bayesian inference and provides an effective framework for capturing and quantifying different sources of uncertainty.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper proposes a novel approach to estimate the confidence of predictions in a regression setting. The approach starts from the standard modelling assuming iid samples from a Gaussian distribution with unknown mean and variances and places evidential priors. This opens the door to online applications with fully integrated uncertainty estimates. \n\nPros:\n1.\tNovel approach to regression (a similar work has been published at NeurIPS last year for classification [3]), but the extension of the work to regression is important.\n2.\tThe experimental results show consistent improvement in performance over a wide base of benchmarks, scales to large vision problems and behaves robustly against adversarial examples.\n3.\tThe presentation of the paper is overall nice, and the Figures are very useful to the general comprehension of the article.\nCons:\n1.\tThe theory of evidence, which is not widely known in the ML community, is not clearly introduced. \nI think that the authors should consider adding a section similar to Section 3 of Sensoy et al. [3] should be considered. Currently, the only step explaining the evidential approach that I found was in section 3.1, in a very small paragraph (between \u201cthe mean of [\u2026] to \\lambda + 2\\alpha.\u201d). I believe that the article would greatly benefit from a more thorough introduction of concepts linked to the theory of evidence.\n2.\tThe authors briefly mention that KL is not well defined between some NIG distributions (p.5) and propose a custom evidence regularizer, but there\u2019s very little insight given on how this connects to/departs from the ELBO approach. \n\nOther comments/questions:\n1.\t(p.1)  I\u2019m not sure to fully understand what\u2019s meant by higher-order/lower-order distributions, could you clarify?\n2.\t(p.3) In section 3.1, the term in the total evidence \\phi_j is not defined.\n3.\t(p.3) Could you comment on the implications of assuming that the estimated distribution can be factorized? \n4.\t(p.4) Could you comment on the difference that there is between NLL_ML and NLL_SOS from a modelling perspective?\n5.\t(p.4) The ELBO loss (6) is unclearly defined, and not connected to the direct context. I would suggest moving this to the section 3.3, where the prior p(\\theta) used in eq. (6) is actually defined.\n\nSpelling & typos:\n-\t(p.4) There are several typos in equation (8), where tau should be replaced with 1/\\sigma^2. \n-\t(p.8) In the last sentence, there is \u201cntwork\u201d instead of network.\n-\t(p.9) There is a typo in the name of J\u00f8sang in the references. \n-\t(p.10) In equation (13), due to the change of variable, there should be a \n-(1/\\tau^2) added;  \n-\t(p.10) In equation (14), the \\exp(-\\lambda*\\pi*(\u2026)) should be replaced with \\exp(-\\lambda*\\tau*(\u2026)). \n\n6: Weak Accept", "turbo-16k-0613_pred": "In this paper, the authors propose a novel approach for training deterministic neural networks that not only estimate target values but also provide robust metrics of model uncertainty. They achieve this by learning evidential distributions and penalizing prediction errors based on the available evidence. The approach is validated on a benchmark regression task and applied to the task of depth estimation in computer vision.\n\nThe results show that the evidential regression approach outperforms competing methods in terms of root mean squared error and negative log-likelihood on multiple datasets. The authors demonstrate that the approach accurately estimates both aleatoric and epistemic uncertainties and successfully captures errors in the predictions. The evidential uncertainty estimates also show good calibration with the model's errors.\n\nFurthermore, the robustness of the approach is tested on out-of-distribution samples and adversarial perturbations, where the evidential models consistently increase uncertainty on these samples. The models also exhibit higher overall uncertainty when faced with adversarial inputs, compared to dropout and ensemble methods.\n\nOverall, this paper presents a valuable contribution to the field of uncertainty estimation in neural networks. The evidential regression approach provides a principled way to estimate uncertainties and can be applied to a wide range of regression tasks. The results demonstrate the effectiveness and scalability of the approach, making it suitable for deployment in safety-critical domains.\n\nOne suggestion for future work could be to explore the application of the evidential regression approach to other domains, such as natural language processing or time series analysis. Additionally, it would be interesting to investigate the computational efficiency of the approach and compare it to other uncertainty estimation methods. Finally, it would be beneficial to conduct a more thorough analysis of the training stability and convergence properties of the approach.\n\nOverall, this paper opens up exciting possibilities for improving the reliability and robustness of neural network predictions through accurate uncertainty estimation.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Answerer in Questioner\u2019s Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses.", "turbo-16k-0613_pred": "In this paper, the authors propose an extension to the Answerer in Questioners Mind (AQM) algorithm called AQM+. The AQM framework is a neural approach to task-oriented dialog problems that aims to generate appropriate questions based on the dialog history. However, the original AQM algorithm had limitations in handling large-scale problems with a large number of candidate classes, questions, and answers. AQM+ overcomes these limitations by employing subset sampling to handle the increased number of questions, answers, and candidate classes. It generates candidate questions and answers at each turn, and then selects the most appropriate one to ask a question. The authors conducted experiments on the GuessWhich task and compared AQM+ with other models. The results showed that AQM+ significantly outperformed the comparative deep learning models in GuessWhich, reducing the error rate by 61.5%. Overall, AQM+ extends the capabilities of the AQM framework and shows promise for practical task-oriented dialog applications.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning cooperative visual dialog agents with deep reinforcement learning , Authors: Abhishek Das Satwik Kottur... , 2017\n2.  Title: Guesswhat?! visual object discovery through multi-modal dialogue , Authors: Harm de Vries Florian Strub... , 2017\n3.  Title: Criteria for human-compatible ai in two-player vision-language tasks , Authors: Cheolho Han Sang-Woo Lee... , 2017\n4.  Title: Two can play this game: Visual dialog with discriminative question generation and answering , Authors: Unnat Jain Svetlana Lazebnik... , 2018\n5.  Title: Nsml: Meet the mlaas platform with a real-world case study , Authors: Hanjoo Kim Minkyu Kim... , 2018\n6.  Title: Codraw: Visual dialog for collaborative drawing , Authors: Jin-Hwa Kim Devi Parikh... , 2017\n7.  Title: Answerer in questioner\u2019s mind for goal-oriented visual dialogue , Authors: Sang-Woo Lee Yu-Jung Heo... , 2018\n8.  Title: Microsoft coco: Common objects in context , Authors: Tsung-Yi Lin Michael Maire... , 2014\n9.  Title: Best of both worlds: Transferring knowledge from discriminative learning to a generative visual dialog model , Authors: Jiasen Lu Anitha Kannan... , 2017\n10.  Title: Query-reduction networks for question answering , Authors: Minjoon Seo Sewon Min... , 2017\n11.  Title: Neural speed reading via skim-rnn , Authors: Minjoon Seo Sewon Min... , 2018\n12.  Title: Visual reference resolution using attention memory for visual dialog , Authors: Paul Hongsuck Seo Andreas Lehrmann... , 2017\n13.  Title: Multimodal hierarchical reinforcement learning policy for task-oriented visual dialog , Authors: Jiaping Zhang Tiancheng Zhao... , 2018\n14.  Title: Goal-oriented visual question generation via intermediate rewards , Authors: Junjie Zhang Qi Wu... , 2018\n", "gt": "A task-oriented visual dialog problem has recently been paid attention in the field of computer vision and natural language processing (Kim et al., 2017). GuessWhat is one of the famous taskoriented dialog tasks, where the goal is to figure out a target object in the image through a dialog that the answerer has in mind (de Vries et al., 2017). However, GuessWhat is relatively an easy task because it only allows the answer form of yes or no. The baseline visual question answering (VQA) model achieves 78.5%. In the object guessing task (i.e., GuessWhat task itself), the state-of-the-art averaged accuracy of SL, RL (Zhang et al., 2018b), and AQM (Lee et al., 2018) reached 44.6% and 60.8%, and 72.9% at the 5th round, respectively. Random guessing baseline has an accuracy of\n16.0% (Han et al., 2017), thus RL algorithms achieve 53.3% error decrease, whereas AQM achieves 67.7%. GuessWhich is a cooperative two-player game that one player tries to figure out an image out of 9,628 that another has in mind (Das et al., 2017b). GuessWhich uses Visual Dialog dataset (Das et al., 2017a) which includes human dialogs on MSCOCO images (Lin et al., 2014) as well as the captions that are generated. Although GuessWhich is similar to GuessWhat, it is more challenging in every task including asking a question, giving an answer, and guessing the target class. For example, unlike GuessWhat that can be answered in yes or no, the answer can be an arbitrary sentence in GuessWhich. Therefore, the VQA task in the Visual Dialog dataset is much studied than the GuessWhat dataset (Lu et al., 2017; Seo et al., 2017b). Similar to GuessWhat, SL and RL approaches have been applied to solve the GuessWhich task and they showed a moderate increase in performance (Das et al., 2017b; Jain et al., 2018; Zhang et al., 2018a). However, based on the authors\u2019 recent Github implementation1 of the papers in ICCV (Das et al., 2017b), SL and RL methods have shown that only 6% of error is diminished through the dialog compared to the zeroth turn baselines which only use generated caption.", "turbo-16k-0613_pred": "In recent years, deep learning has made significant advancements in the field of task-oriented dialog systems. One approach is to use sequence-to-sequence models trained in a supervised or reinforcement learning fashion to generate appropriate responses. However, these approaches often struggle to handle unseen scenarios and maintain consistency in the dialog. To address these challenges, Lee et al. (2018) proposed the Answerer in Questioner's Mind (AQM) algorithm, which treats the problem as a twenty questions game and selects the question with maximum information gain. AQM showed promising results in task-oriented visual dialog tasks such as GuessWhat, but its scalability was limited due to the computational complexity. To overcome this limitation, the authors introduced AQM+, a more generalized version of AQM. AQM+ employs an approximation based on subset sampling to handle large-scale problems with a large number of questions, answers, and candidate classes. It generates candidate questions and answers at each turn and selects the most contextual question based on the previous dialog. Experimental results on the GuessWhich task showed that AQM+ significantly outperformed comparative models, leading to a 61.5% decrease in error. The study also conducted ablation studies and analysis, providing insights into the efficiency and practicality of AQM+.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes an improvement over the AQM approach for an information-theoretic framework for task-oriented dialog systems. Specifically, the paper tries to circumvent the problem of explicitly calculating the information gain while asking a question in the AQM setting. While the original AQM approach sweeps over all possible guesses and answers while estimating information gain, this is rendered impractical in scenarios where this space cannot be tractably enumerated. As a solution, AQM+ proposes sweeping over only some top-k relevant instantiations of answers and guesses in this space by normalizing the probabilities of the subset of the space in consideration. \n\nComments:\n- The paper is overloaded with notations and the writing is not very smooth. The terse nature of the content makes it hard to follow in general. If someone apriori was not familiar with task-oriented dialog or the visual dialog setting in Das et al. (2017b), it would be quite hard to follow.\n- While mentioning SL/RL approaches while comparing or introducing the setup, the authors do not make any distinction between discriminative and generative dialog models. Specifically, SL approaches could either be trained discriminatively to rank options among the provided ones given dialog context or in a generative manner via token-level teacher forcing. The authors should clearly make this distinction in the introduction and in other places where it\u2019s needed.\n- The authors should stress more upon the approximations involved while calculating mutual information. As far as I understand, even in the AQM approach the numerator and the denominator within the logarithm are estimated from a different set of parameters and as such they need not be consistent with each other under marginalization. The term resembles MI and ensuring consistency in such a framework would require either of the numerator or the denominator to be close to something like a variational approximation of the true distribution. In addition, AQM+ adopts the same framework as AQM but computes MI over some top-k of the random variables being considered. Could the authors comment more on why restricting the space of r.v.\u2019s to some top-k samples is a good idea? Would that not lead to somewhat of a biased estimator?\n- Unless I am missing something, training aprxAgen from the training data (indA) seems odd. Assuming, this to be Qbot\u2019s mental model of Abot -- there is no prior reason why this should be initialized or trained in such a manner. Similarly, the training paradigm of the depA setting is confusing. If they are trained in a manner similar to a regular Abot -- either SL or RL -- then they\u2019re not approximate mental models but are rather just another Abot agent in play which is being queried by\n\n6: Marginally above acceptance threshold", "turbo-16k-0613_pred": "In this paper, the authors propose the AQM+ algorithm as an extension of the AQM framework for handling more general and complex task-oriented dialog tasks. The AQM+ algorithm addresses the limitations of previous approaches by generating candidate questions and answers at every turn, considering the context of the dialog. The algorithm uses an approximation based on subset sampling to handle the large number of questions, answers, and candidate classes. The authors conducted experiments on the GuessWhich task and compared AQM+ with other models. The results showed that AQM+ outperformed the comparative models and significantly reduced the error rate during the dialog. The authors also conducted ablation studies, which confirmed the effectiveness of the AQM+ algorithm. Overall, the experimental results provided meaningful insights on how the AQM framework can further improve task-oriented dialog systems. \n\nIn my opinion, this paper presents a significant contribution to the field of task-oriented dialog systems. The AQM+ algorithm offers a novel approach to generating questions in complex dialog scenarios, and it effectively handles the large search space of questions, answers, and candidate classes. The experimental results demonstrate the superiority of AQM+ over other models, and the ablation studies provide valuable insights into the factors that contribute to its performance. However, there are a few points that need to be addressed in future work. Firstly, the paper focuses on a specific task (GuessWhich), and it would be interesting to see how the AQM+ algorithm performs on other task-oriented dialog tasks. Additionally, the computational cost of the algorithm should be further optimized to facilitate faster inference. Finally, it would be beneficial to explore ways to apply online learning techniques to AQM+ in order to adapt the model to individual user preferences. Overall, this paper provides a strong foundation for future research in the field of task-oriented dialog systems.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recent studies have shown that reinforcement learning (RL) models can be vulnerable in various scenarios, where noises from different sources could appear. For instance, the observed reward channel is often subject to noise in practice (e.g., when observed rewards are collected through sensors), and thus observed rewards may not be credible. Also, in applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors. In this paper, we consider noisy RL problems where observed rewards by RL agents are generated with a reward confusion matrix. We call such observed rewards as perturbed rewards. We develop an unbiased reward estimator aided robust RL framework that enables RL agents to learn in noisy environments while observing only perturbed rewards. Our framework draws upon approaches for supervised learning with noisy data. The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 67.5% and 46.7% improvements in average on five Atari games, when the error rates are 10% and 30% respectively.", "turbo-16k-0613_pred": "This paper presents a framework for reinforcement learning in the presence of noisy rewards. The authors propose an unbiased reward estimator that allows an RL agent to learn in a noisy environment by using surrogate rewards. The framework is built upon existing RL algorithms such as Q-Learning, Cross-Entropy Method, Deep Q-Network, and more. The authors analyze the convergence and sample complexity of their method and conduct experiments on various games from OpenAI Gym. The results show that their reward robust RL method achieves comparable performance to the policy trained using true rewards, and in some cases even outperforms it. The contributions of this work are summarized as follows: (1) the adaptation and generalization of unbiased estimators for true rewards using surrogate rewards in reinforcement learning, (2) the analysis of convergence and sample complexity of the reward robust RL methods, and (3) extensive experiments on OpenAI Gym games demonstrating the robustness of the proposed algorithms under noisy environments.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning robust rewards with adversarial inverse reinforcement learning , Authors: Justin Fu Katie Luo... , 2017\n2.  Title: Continuous deep q-learning with model-based acceleration , Authors: Shixiang Gu Timothy P. Lillicrap... , 2016\n3.  Title: Adversary a3c for robust reinforcement learning, 2018 , Authors: Zhaoyuan Gu Zhenzhong Jia... , 2018\n4.  Title: Adversarial attacks on neural network policies , Authors: Sandy Huang Nicolas Papernot... , 2017\n5.  Title: Deep reinforcement learning doesn\u2019t work yet , Authors: Alex Irpan , 2018\n6.  Title: Reinforcement learning in robust markov decision processes , Authors: Shiau Hong Lim Huan Xu... , 2016\n7.  Title: Tactics of adversarial attack on deep reinforcement learning agents , Authors: Yen-Chen Lin Zhang-Wei Hong... , 2017\n8.  Title: Learning from corrupted binary labels via class-probability estimation , Authors: Aditya Menon Brendan Van Rooyen... , 2015\n9.  Title: Learning with noisy labels , Authors: Nagarajan Natarajan Inderjit S Dhillon... , 2013\n10.  Title: Robust adversarial reinforcement learning , Authors: Lerrel Pinto James Davidson... , 2017\n11.  Title: Epopt: Learning robust neural network policies using model ensembles , Authors: Aravind Rajeswaran Sarvjeet Ghotra... , 2016\n12.  Title: Reinforcement learning under model mismatch , Authors: Aurko Roy Huan Xu... , 2017\n13.  Title: A rate of convergence for mixture proportion estimation, with application to learning from noisy labels , Authors: Clayton Scott , 2015\n14.  Title: Classification with asymmetric label noise: Consistency and maximal denoising , Authors: Clayton Scott Gilles Blanchard... , 2013\n15.  Title: Distral: Robust multitask reinforcement learning , Authors: Yee Whye Teh Victor Bapst... , 2017\n", "gt": "Robust Reinforcement Learning It is known that RL algorithms are vulnerable to noisy environments (Irpan, 2018). Recent studies (Huang et al., 2017; Kos & Song, 2017; Lin et al., 2017) show that learned RL policies can be easily misled with small perturbations in observations. The presence of noise is very common in real-world environments, especially in robotics-relevant applications. Consequently, robust (adversarial) reinforcement learning (RRL/RARL) algorithms have been widely studied, aiming to train a robust policy which is capable of withstanding perturbed observations (Teh et al., 2017; Pinto et al., 2017; Gu et al., 2018) or transferring to unseen environments (Rajeswaran et al., 2016; Fu et al., 2017). However, these robust RL algorithms mainly focus on noisy vision observations, instead of the observed rewards. A couple of recent works (Lim et al., 2016; Roy et al., 2017) have also looked into a rather parallel question of training robust RL algorithms with uncertainty in models. Learning with Noisy Data Learning appropriately with biased data has received quite a bit of attention in recent machine learning studies Natarajan et al. (2013); Scott et al. (2013); Scott (2015); Sukhbaatar & Fergus (2014); van Rooyen & Williamson (2015); Menon et al. (2015). The idea of above line of works is to define unbiased surrogate loss function to recover the true loss using the knowledge of the noises. We adapt these approaches to reinforcement learning. Though intuitively the idea should apply in our RL settings, our work is the first one to formally establish this extension both theoretically and empirically. Our quantitative understandings will provide practical insights when implementing reinforcement learning algorithms in noisy environments.", "turbo-16k-0613_pred": "In recent years, there has been growing interest in the field of reinforcement learning (RL) in dealing with noisy reward signals. Noisy rewards are common in real-world applications, where sensors might be affected by various sources of noise and adversarial attacks can manipulate the reward values. Designing a suitable reward function is crucial for building RL models that can effectively learn in noisy environments. However, it is challenging to design a reward function that produces credible rewards in the presence of noise.\n\nTo address this challenge, the authors propose a reward robust RL framework that enables RL agents to learn in noisy environments with only perturbed rewards. The framework is built on existing RL algorithms, such as Q-Learning, Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), and incorporates an efficient and flexible estimation module to estimate the reward confusion matrices. The estimations are based on observed noisy rewards and a predicted true reward using majority voting.\n\nThe authors conduct extensive experiments on various games, including CartPole, Pendulum, and Atari games, to evaluate the performance of the proposed framework. The results show that the reward robust RL method achieves comparable performance with the policy trained using true rewards and even outperforms it in some cases. The experiments also demonstrate the robustness of the framework under different levels of noise.\n\nIn conclusion, the authors propose a robust RL framework that addresses the issue of learning with noisy rewards. The framework shows promising results in various real-world applications and provides a valuable approach for designing RL models that can effectively learn in noisy environments.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "the paper focuses on an interesting and relatively unexplored direction for RL. Apart from the work cited by the paper (and perhaps work like Krueger et al. (2016), in which agents must pay some cost to observe true rewards), there is little work on learning settings of this kind. This paper represents a first step in gaining clarity on how to formalize and study this problem. I did, however, find the analysis and the experiments to be relatively disjointed -- the main sample complexity result presented by the paper (Theorem 2) was given for Phased Q-Learning, yet no experiments actually evaluate the performance of Phased Q-Learning. I think the paper could benefit from experiments focused on simple domains that showcase how traditional algorithms do in cases where it is easier to understand (and visualize) the impact of the reward perturbations (simple chain MDPs, grid worlds, etc.) -- and specifically experiments including Phased Q-Learning. \n\nSection 1) Introduction:\n\t- \"adversaries in real-world\" --> \"adversaries in the real-world\"\n\t- You might consider citing Loftin et al. (2014) regarding the bulleted point about \"Application-Specific Noise\".\n\t- \"unbiased reward estimator aided reward robust reinforcement learning framework\" --> this was a bit hard to parse. Consider making more concise, like: \"unbiased reward estimator for use in reinforcement learning with perturbed rewards\".\n\t- \"Our solution framework builds on existing reinforcement learning algorithms, including the recently developed DRL ones\" --> cite these up front So, cite: Q-Learning, CEM, SARSA, DQN, Dueling DQN, DDPG, NAF, and PPO, and spell out the acronym for each the first time you introduce them.\n\t- \"layer of explorations\" --> \"layer of exploration\"\n\nSection 2) Problem Formulation\n\t- \"as each shot of our\" --> what is 'shot' in this context?\n\t- \"In what follow,\" --> \"In what follows,\"\n\t- \"where 0 < \\gamma \\leq 1\" --> Usually, $\\gamma \\in [0,1)$, or $[0,1]$. Why can't $\\gamma = 0$?\n\t- The transition notation changes between $\\mathbb{P}_a(s_{t+1} | s_t)$ and $\\mathbb{P}(s_{t+1} | s_t, a_t)$. I'd suggest picking one and sticking with it to improve clarity.\n\t- \"to learn a state-action value function, for example the Q-function\" --> Why is the Q-function just an example? Isn't is *the* state-action value function? That is, I'd suggest replacing \"to learn a state-action value function, for example the Q-function\" with \"to learn a state-action value function, also called the Q-function\"\n\t- \"Q-function calculates\" --> \"The Q-function denotes\"\n\t- \"the reward feedbacks perfectly\" --> \"the reward feedback perfectly\"\n\t- I prefer that the exposition of the perturbed reward MDP be done with C in the tuple. So: $\\tilde{M} = \\langle \\mathcal{S}, \\mathcal{A}, \\mathcal{R}, C, \\mathcal{P}, \\gamma \\rangle$. This seems the most appropriate definition, since the observed rewards will be generated by $C$.\n\t- The setup of the confusion matrix for reward noise over is very clean. It might be worth pointing out that $C$ need not be Markovian. There are cases where C is not just a function of $\\mathcal{S}$ and $\\mathcal{R}$, like the adversarial case you describe early on.\n\n\n4) Experiments:\n\t- Diverse experiments! That's great. Lots of algorithms, lots of environment types.\n\t- I expected to see Phrased Q-Learning in the experiments. Why was it not included?\n\t- The plots are pretty jagged, so I'm left feeling a bit skeptical about some of the results. The results would be strengthened if the experiments were repeated for more trials.\n\n6: Marginally above acceptance threshold", "turbo-16k-0613_pred": "In this paper, the authors introduce a reward robust reinforcement learning (RL) framework for dealing with noisy rewards. They propose the use of surrogate rewards, estimated using a reward confusion matrix, to address the bias introduced by noisy observations. The authors analyze the convergence and sample complexity of their reward robust RL methods and conduct extensive experiments on various environments, including classic control games and Atari games. The results show that their method successfully enables RL agents to learn in noisy environments, achieving comparable performance to learning with true rewards and even outperforming it in some cases. The authors also discuss the estimation of the reward confusion matrices and highlight the trade-off between bias and variance in the surrogate rewards. Overall, the paper presents a novel approach to handle noisy rewards in RL and provides insights into the effectiveness of surrogate rewards in improving RL performance in noisy environments.\n\nSuggestions:\n1. The paper could provide more details on the specific experiments conducted, such as the specific noise levels tested and the number of trials for each experiment.\n2. The paper could include more discussion on the limitations of the proposed approach and potential directions for future research.\n3. It would be useful to provide more insights into the benefits and drawbacks of using surrogate rewards compared to learning with true rewards in noisy environments.\n4. The paper could provide more information on the computational complexity and scalability of the proposed algorithms, particularly in larger state spaces or more complex environments.\n5. It would be helpful to explain the implications of the results obtained in terms of their applicability to real-world RL applications and potential practical implementations.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain QA datasets where it outperforms the existing state-of-the-art, notably leading to a relative improvement of 8.1% on the AI2 Reasoning Challenge (ARC) dataset.", "turbo-16k-0613_pred": "This paper proposes an essential-term-aware Retriever-Reader (ET-RR) model for open-domain question answering (QA). The model addresses the challenges of dealing with questions with little available evidence, free-form text answers, and complex questions requiring reasoning over multiple passages. The ET-RR model consists of an essential term selector and an attention-enhanced reader. The essential term selector uses a recurrent neural network to identify key words from the question, which are then concatenated with each answer choice to formulate a query. The query is sent to a retrieval engine to obtain related evidence. The attention-enhanced reader takes the question, answer choices, and retrieved passage as input and uses attention and fusion layers to obtain a refined representation of the text sequences. A choice-interaction module handles the semantic relations between answer choices. Experimental results show that the ET-RR model achieves high accuracy on the ARC dataset, ranking first on the leaderboard, and outperforms baseline models on the RACE-Open and MCScript-Open datasets. Ablation studies demonstrate the effectiveness of each component of the model. Overall, the ET-RR model demonstrates superior performance in open-domain QA tasks.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Think you have solved question answering? try arc, the ai2 reasoning challenge , Authors: Peter Clark Isaac Cowhey... , 2018\n2.  Title: Efficient and robust question answering from minimal context over documents , Authors: Sewon Min Victor Zhong... , 2018\n3.  Title: Retrieve-and-read: Multi-task learning of information retrieval and reading , Authors: Kyosuke Nishida Itsumi Saito... , 2018\n4.  Title: Yuanfudao at semeval-2018 task 11: Three-way attention and relational knowledge for commonsense machine comprehension , Authors: Liang Wang , 2018\n5.  Title: Reinforced ranker-reader for open-domain question answering , Authors: Shuohang Wang Mo Yu... , 2018\n", "gt": "There has recently been growing interest in building better retrievers for open-domain QA. Wang et al. (2018) proposed a Reinforced Ranker-Reader model that ranks retrieved evidence and assigns different weights to evidence prior to processing by the reader. Min et al. (2018) demonstrated that for several popular MRC datasets (e.g. SQuAD, TriviaQA) most questions can be answered using only a few sentences rather than the entire document. Motivated by this observation, they built a sentence selector to gather this potential evidence for use by the reader model. Nishida et al. (2018) developed a multi-task learning (MTL) method for a retriever and reader in order to obtain a strong retriever that considers certain passages including the answer text as positive samples during training. The proposed MTL framework is still limited to the scenario when it is feasible to discover whether the passages contain the answer span. Although these works have achieved progress on open-domain QA by improving the ranking or selection of given evidence, few have focused on the scenario where the model needs to start by searching for the evidence itself. Scientific Question Answering (SQA) is a representative open-domain task that requires capability in both retrieval and reading comprehension. In this paper, we study question answering on the AI2 Reasoning Challenge (ARC) scientific QA dataset (Clark et al., 2018). This dataset contains elementary-level multiple-choice scientific questions from standardized tests and a large corpus of relevant information gathered from search engines. The dataset is partitioned into \u201cChallenge\u201d and \u201cEasy\u201d sets.", "turbo-16k-0613_pred": "In recent years, there has been significant research on open-domain question answering (QA). Many existing works have focused on the \"search-and-answer\" strategy and have achieved strong performance on various QA datasets such as TriviaQA, SQuAD, and MS-Macro (Chen et al., 2017; Kwon et al., 2018; Wang et al., 2018). However, open-domain QA tasks become more difficult when dealing with questions with little evidence, free-form text answers, or complex questions that require reasoning over multiple passages. To address these difficulties, the authors propose an essential-term-aware Retriever-Reader (ET-RR) model that incorporates commonsense knowledge and improves retrieval capability.\n\nThe ET-RR model consists of two stages: an essential term selector and an attention-enhanced reader. The essential term selector, called ET-Net, uses a recurrent neural network to understand the question and select essential terms. These essential terms are then concatenated with each answer choice and used as a query for the retrieval engine to obtain relevant evidence. The attention-enhanced reader takes the question, answer choices, and retrieved evidence as input and uses a sequence of language understanding layers to predict the correct answer. Additionally, a choice-interaction module is added to handle the semantic relations between answer choices.\n\nThe performance of the ET-RR model is evaluated on the ARC dataset, where it achieves an accuracy of 36.61% on the test set, ranking first on the official leaderboard. The model is also evaluated on the RACE-Open and MCScript-Open datasets, where it outperforms baseline models by a large margin. Ablation studies show that each component of the model contributes to its accuracy.\n\nIn related work, Clark et al. (2018) introduce the ARC dataset, which poses challenges in retrieving question-related evidence. Min et al. (2018) propose an efficient and robust QA approach that relies on minimal context over documents. Nishida et al. (2018) present a multi-task learning approach for information retrieval and reading. Wang (2018) explores the use of relational knowledge and attention mechanisms for commonsense machine comprehension. Wang et al. (2018) propose a reinforced ranker-reader model for open-domain question answering. These papers provide valuable insights and techniques that are related to the ET-RR model and the challenges in open-domain QA.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper has made two major contributions: (1) a new neural reader architecture for multi-choice QA; (2) it is the first to introduce essential term selection to open-domain QA, to the best of my knowledge. The above two proposed modules (reader and selector) are not very novel, but are still valid contributions to me. Experiments on the ARC dataset shows that (1) the proposed reader itself improves over the state-of-the-art on the leaderboard; (2) introducing the essential term selector further improves the above results by about 2%.\n\nAlthough the paper also provides additional experiments on other datasets, I feel that the contributions of the proposed methods are not sufficiently verified. I would suggest the authors consider the following further experiments that I believe could improve its ratings:\n\n(1) The proposed reader works very well on ARC. However, besides the BiDAF, there is no comparison between the proposed reader and previous models on datasets other than ARC. In order to know whether the result generalizes or not, I think the authors should conduct experiments on the regular RACE or other multi-choice QA datasets, to fully test the reader model.\n\n(2) It is not clear whether the essential term selector could help on datasets other than science questions. Again, the authors reported results on two other datasets. However, on neither of these datasets the ET-RR was compared with ET-RR (Concat). Therefore, I have concerns that the proposed framework may only be significant on the ARC dataset.\n\nMoreover, it will be interesting to see whether the essential term selector can be learned from distant supervision. For example, using REINFORCE to learn the selector model with the rewards from the end-task performance. The current framework heavily relies on the supervised training data from (Khashabi et al., 2017), which may limit its usage to other datasets.\n5: Marginally below acceptance threshold", "turbo-16k-0613_pred": "Review:\n\nThe paper proposes a new retriever-reader model called ET-RR for open-domain question answering (QA). The model incorporates an essential-term selector (ET-Net) that helps in understanding key words in a question and formulates more efficient queries for retrieving relevant evidence. The model also includes an attention-enhanced reader that captures the relationships between passages, questions, and answer choices. \n\nThe paper provides a detailed explanation of the model architecture and demonstrates its effectiveness on three datasets: ARC, RACE-Open, and MCScript-Open. ET-RR achieves state-of-the-art performance on ARC, outperforming other existing models. It also shows significant improvements on RACE-Open and MCScript-Open datasets compared to IR solver and BiLSTM Max-out model. \n\nThe paper includes ablation studies that demonstrate the importance of each component in the ET-RR model. It shows that all attention components contribute to the model's performance, and the choice interaction layer further improves accuracy. The essential-term selector is shown to be crucial for effective query formulation, outperforming TF-IDF-based approaches.\n\nOverall, the paper is well-written and provides clear explanations of the model and its components. The experimental results support the claims made by the authors and show the superiority of the proposed model. \n\nSuggestions: \n1. It would be helpful to provide more details about the datasets used in the experiments, such as the number of questions and the distribution of question lengths.\n2. Explaining the limitations of the model and future directions for improvement would further enhance the paper.\n3. Considering additional evaluation metrics, such as precision and recall, would provide a more comprehensive analysis of the model's performance.\n\nQuestions:\n1. What are the potential applications of the proposed ET-RR model beyond open-domain question answering?\n2. Can the essential-term selector (ET-Net) be adapted for other tasks in natural language processing?\n3. Are there any plans to release the code and pre-trained models for ET-RR to facilitate further research in this area?", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to the dense connections of the internal structure, which leads to high computational costs in training such dense networks. To address this issue, we design a reinforcement learning framework to search for efficient DenseNet architectures with layer-wise pruning (LWP) for different tasks, while retaining the original advantages of DenseNet, such as feature reuse, short paths, etc. In this framework, an agent evaluates the importance of each connection between any two block layers, and prunes the redundant connections. In addition, a novel reward-shaping trick is introduced to make DenseNet reach a better trade-off between accuracy and float point operations (FLOPs). Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives.", "turbo-16k-0613_pred": "This paper proposes a layer-wise pruning method for compressing and accelerating DenseNet, a deep neural network architecture. The method uses reinforcement learning with an LSTM controller to learn to prune weights and connections in the network while maintaining good accuracy. The agent generates a sequence of actions and receives rewards based on the network structure's performance on the validation dataset. The paper also introduces a curriculum of exploration to facilitate effective network pruning. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that the proposed method significantly reduces the number of parameters and floating-point operations, while maintaining or slightly degrading prediction performance. The results show that the compressed DenseNet architectures achieve a balance between inference accuracy and computational resources. In conclusion, the paper presents a promising approach for optimizing the efficiency of deep neural networks on low-end devices.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Multi-scale dense networks for resource efficient image classification , Authors: Gao Huang Danlu Chen... , 2017\n2.  Title: Densely connected convolutional networks , Authors: Gao Huang Zhuang Liu... , 2017\n3.  Title: Condensenet: An efficient densenet using learned group , Authors: Gao Huang Shichen Liu... , 2018\n4.  Title: Blockdrop: Dynamic inference paths in residual networks. 2018 , Authors: Zuxuan Wu Tushar Nagarajan... , 2018\n", "gt": "Huang et al. (2018) proposed group convolution to remove connections between layers in DenseNet for which this feature reuse is superfluous; Huang et al. (2017a) also suggested progressively update prediction for every test sample to unevenly adapt the amount of computational resource at inference time. The most related work is BlockDrop (Wu et al., 2018), which used reinforcement learning to prune weight dynamically at inference time but can only be applied to ResNet or its variants. In contrast, our approach is based on DenseNet, aiming to find efficient network structure based the densely connected features of DenseNet.", "turbo-16k-0613_pred": "In recent years, deep neural networks have been widely used on mobile devices, but their computational costs pose a challenge for real-time inference. To address this issue, researchers have focused on compressing and accelerating deep neural networks. One particularly promising architecture is DenseNet, which introduces direct connections between layers and achieves state-of-the-art results on various datasets. However, recent studies have shown that there is high redundancy in DenseNet. This paper proposes a layer-wise pruning method for DenseNet using reinforcement learning. An agent learns to prune weights and connections while maintaining accuracy on a validation dataset. The agent generates a sequence of actions and receives rewards based on the network structure. Additionally, the agent explores different architectures to effectively prune neural networks. Experimental results on competitive datasets demonstrate that the proposed method significantly reduces the number of parameters and computational costs while maintaining or slightly degrading prediction performance. The approach strikes a balance between inference accuracy and computational resources. Overall, this work provides a novel method to compress and accelerate DenseNet without extensive expert knowledge on network design. Related works such as Multi-scale DenseNet, CondenseNet, and BlockDrop are cited to demonstrate the background and relevance of the proposed method.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper introduces RL based approach to prune layers in a DenseNet. This work extends BlockDrop to DenseNet architecture making the controller independent form the input image. The approach is evaluated on CIFAR10 and CIFAR100 datasets as well as on ImageNet showing promising results.\n\nIn order to improve the paper, the authors could take into consideration the following points:\n1. Given the similarity of the approach with BlockDrop, I would suggest to discuss it in the introduction section clearly stating the similarities and the differences with the proposed approach. \n2. BlockDrop seems to introduce a general framework of policy network to prune neural networks. However, the authors claim that BlockDrop \"can only be applied to ResNets or its variants\". Could the authors comment on this? \n3. In the abstract, the authors claim: \"Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives\". It is hard to asses if the statement is correct given the evidence presented in the experimental section. It is not clear if the method is more efficient and compact than others, e. g.  CondenseNet. \n4. In the experimental section, addressing the following questions would make the section stronger: What is more important FLOPs or number of parameters? What is the accuracy drop we should allow to pay for reduction in number of parameters or FLOPs?\n5. For the evaluation, I would suggest to show that the learned policy is better than a random one: e. g. not using the controller to define policy (in line 20 of the algorithm) and using a random random policy instead.\n6. In Table 1, some entries for DenseNet LWP are missing. Is the network converging for this setups? \n7. \\sigma is not explained in section 3.3. What is the intuition behind this hyper parameter?\n\n\nAdditional comments:\n1. It might be interesting to compare the method introduced in the paper to a scenario where the controller is conditioned on an input image and adaptively selects the connections/layers in DenseNet at inference time.\n2. It might be interesting to report the number of connections in Table 1 for all the models.\n\n4: Ok but not good enough - rejection", "turbo-16k-0613_pred": "In this paper, the authors propose a method called layer-wise pruning (LWP) for compressing and accelerating DenseNet, a popular neural network architecture. They utilize reinforcement learning, specifically an LSTM controller, to learn to prune weights and connections in the DenseNet while maintaining good accuracy on validation datasets. The LSTM controller generates a sequence of actions to prune connections and receives rewards based on the performance of the pruned network. The authors conduct extensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets to evaluate the effectiveness of their method.\n\nThe results show that the LWP method significantly reduces the number of parameters and floating point operations (FLOPs) in the DenseNet while maintaining comparable prediction performance. For example, on CIFAR-10 dataset, the method achieves a reduction of approximately 78.2% in parameters and 81.4% in FLOPs with only a slight increase in test error. The experiments also demonstrate that the LWP method outperforms other compression methods such as Condensenet and neural architecture search (NAS).\n\nOverall, this paper presents a promising approach to compressing and accelerating DenseNet using reinforcement learning. The results indicate that the proposed method can effectively reduce the computational resources required by DenseNet while maintaining satisfactory prediction performance. However, it would be beneficial to see further analysis on the trade-off between performance and complexity, as well as the sensitivity of the method to different hyperparameters. Additionally, it would be interesting to investigate the applicability of the LWP method to other neural network architectures and datasets.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "The non-convex nature of the loss landscape of deep neural networks (DNN) lends them the intuition that over the course of training, stochastic optimization algorithms explore different regions of the loss surface by entering and escaping many local minima due to the noise induced by mini-batches. But is this really the case? This question couples the geometry of the DNN loss landscape with how stochastic optimization algorithms like SGD interact with it during training. Answering this question may help us qualitatively understand the dynamics of deep neural network optimization. We show evidence through qualitative and quantitative experiments that mini-batch SGD rarely crosses barriers during DNN optimization. As we show, the mini-batch induced noise helps SGD explore different regions of the loss surface using a seemingly different mechanism. To complement this finding, we also investigate the qualitative reason behind the slowing down of this exploration when using larger batch-sizes. We show this happens because gradients from larger batch-sizes align more with the top eigenvectors of the Hessian, which makes SGD oscillate in the proximity of the parameter initialization, thus preventing exploration.", "turbo-16k-0613_pred": "The paper investigates the behavior of stochastic gradient descent (SGD) optimization algorithm in deep neural networks (DNNs). It addresses the non-convexity of the DNN loss surface and how it affects the optimization process. The authors complement the diffusion perspective of SGD with a qualitative view of how SGD explores different regions of the loss landscape. They show that SGD rarely crosses barriers along its path during training. Instead, it moves over the barriers, which suggests an alternate mechanism for exploration. Furthermore, the paper discusses the effect of batch size on exploration. It shows that larger batch sizes slow down the exploration process due to an oscillation behavior of SGD, which is caused by the alignment of mini-batch gradients with the top eigenvectors of the Hessian. The findings are supported by experiments on multiple datasets, architectures, and hyper-parameter settings. The paper concludes by highlighting the practical implications of these findings for setting optimization hyperparameters.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Qualitatively characterizing neural network optimization problems , Authors: Ian J Goodfellow Oriol Vinyals... , 2014\n2.  Title: Train longer, generalize better: closing the generalization gap in large batch training of neural networks , Authors: Elad Hoffer Itay Hubara... , 2017\n3.  Title: On large-batch training for deep learning: Generalization gap and sharp minima , Authors: Nitish Shirish Keskar Dheevatsa Mudigere... , 2016\n4.  Title: Batch size matters: A diffusion approximation framework on nonconvex stochastic gradient descent , Authors: Chris Junchi Li Lei Li... , 2017\n5.  Title: Visualizing the loss landscape of neural nets , Authors: Hao Li Zheng Xu... , 2017\n6.  Title: Stochastic gradient descent as approximate bayesian inference , Authors: Stephan Mandt Matthew D Hoffman... , 2017\n", "gt": "There have been previous work on visualizing the loss surface although from different motivations. Perhaps Goodfellow et al. (2014) is most similar to our work since we use the loss interpolation tool suggested in their paper to perform our analysis. They perform interpolation between the initial and final parameters and based on their finding, draw the conclusion that the loss along the line connecting these two points does not have any barriers. We note that we use their tool for a different purpose and our conclusions are fundamentally different from theirs because we use the observations to investigate whether SGD crosses barriers during optimization over deep networks\u2019 loss landscape. Li et al. (2017b) also visualize the loss landscape of different network architectures. Our work is closely related to a number of recent papers that study SGD as a diffusion process because we present a complementary qualitative view to an aspect of their theory. Hoffer et al. (2017) hypothesize this view based on the evidence that the parameter distance moved by SGD from initialization as a function of the number of iterations resembles a diffusion process. Li et al. (2017a) hypothesize this behavior of SGD and theoretically show that this diffusion process would allow SGD to escape sharp local minima. The authors use this theoretical result to support the findings of Keskar et al. (2016) who find that SGD with small mini-batch size find wider minima. Kushner & Yin (2003); Mandt et al.", "turbo-16k-0613_pred": "In recent years, optimizing deep neural networks (DNNs) has become increasingly challenging due to the non-convex nature of the loss surface. The behavior of optimization algorithms in this setting is less intuitive compared to convex optimization. Additionally, optimizing DNNs is not just about finding any minimum, but finding ones that generalize well. Previous research has shown that stochastic gradient descent (SGD) in DNNs can be viewed as a diffusion process, simulating a random walk on the loss surface. However, new evidence suggests that SGD rarely crosses barriers in the loss surface, indicating a different mechanism for exploration. The slow down in exploration for larger batch sizes is shown to be due to oscillation behavior in SGD, caused by mini-batch gradients aligning with the top eigenvectors of the Hessian. These findings are supported by experiments conducted on multiple datasets, architectures, and hyper-parameter settings.\n\nOther related work in this area includes the characterization of neural network optimization problems (Goodfellow et al., 2014), the study of large-batch training and its effects on generalization (Hoffer et al., 2017), the analysis of batch size on non-convex SGD (Li et al., 2017), the visualization of the loss landscape of neural nets (Li et al., 2017), and the connection between SGD and Bayesian inference (Mandt et al., 2017). These papers provide further insights into the optimization dynamics of DNNs and offer practical guidelines for setting hyperparameters.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The subject of how a given algorithm explores the landscape is still a poorly understood area in training neural networks. There is a large body of recent work that attempts to shed light on this puzzle, and each one tries to claim their share in the furthering of the understanding of the relationship between the geometry of the landscape and the dynamics that one chooses in optimization. The present paper is a fine addition to the literature with interesting observations and novel questions, however, it falls short in many core areas: An apparent work in progress that has a great potential. \n\nA major question (that I hope will easily be addressed) is on the definition of the barrier itself. According to the text, a barrier is defined judging by the minima of two 1-dimensional segments that connect weights connecting three consecutive steps: if the minimum of the line segment defined by the latter step is larger than the former, then it declared that a barrier is crossed. In a low dimensional world, this makes total sense, however, I fail to understand what kind of barrier it implies on the geometry of the landscape: Can the 1-dimensional lines be on the sides of a valley? Can one find *another* 1-dimensional projection for which the inequality is broken? How do such dependencies change the understanding of the problem? And if one is indeed only interested in the flat line segments (since SGD is making discrete steps), then one can, in principle, observe barrier crossing in a convex problem, as well? Is there an argument for otherwise? Or if it is a notion that applies equally well in a convex case then how should we really think about the barrier crossing? On the opposite point of view, can one not imagine a barrier crossing that doesn't appear in this triangular inequality above?\n\nThe paper is full of empirical evidence that is guided by a simple observable that is very intuitive, however, it lacks a comprehensive discussion on the new quantity they propose that I consider a major flaw, but that I think (hope) that the authors can fix very easily. Some minor points that would improve the readability and clarity for the reader:\n- The figures are not very reader-friendly, this can be improved by better using the whitespaces in the paper but it can also be improved by finding further observables that would summarize the observations instead of showing individual consecutive line interpolations.\n- What are the values of the y-axis in Figure 5 and 6? Are they the top eigenvalues of the Hessian?\n- In the models that are compared in Figure 7, what are their generalization properties (early stopping and otherwise)?\n- The interpretation at the end of p. 6 may be a good motivation for the reader if it had been introduced earlier for that section.\n\n\n4: Ok but not good enough - rejection", "turbo-16k-0613_pred": "Review of \"Exploring the Optimization Trajectory of Deep Neural Networks\"\n\nIn \"Exploring the Optimization Trajectory of Deep Neural Networks,\" the authors provide a valuable insight into the behavior of optimization algorithms in the non-convex setting of deep neural networks. The paper investigates how stochastic gradient descent (SGD) explores different regions of the loss landscape by studying the existence of barriers along the optimization path. The authors show that SGD rarely crosses barriers, but instead moves over them, using an alternate mechanism for exploration.\n\nThe paper also analyzes the effect of batch size on exploration. The authors argue that larger batch sizes lead to a higher alignment between the mini-batch gradients and the sharp directions of the loss surface, resulting in more oscillation and slower exploration.\n\nThe empirical analysis presented in the paper is thorough and compelling, using multiple datasets, architectures, and hyper-parameter settings. The results consistently support the authors' claims and provide important insights into the behavior of SGD in deep neural networks.\n\nOverall, this paper makes a significant contribution to the understanding of optimization algorithms in deep learning. The findings have practical implications for setting optimization hyper-parameters and can potentially guide improvements in training deep neural networks.\n\nSuggestions for improvement:\n- The paper could benefit from a more in-depth discussion of the limitations and potential extensions of the proposed analysis, particularly regarding different architectures, loss functions, and activation functions.\n- The authors could provide more context and discussion about the implications of their findings for practical applications of deep learning, such as improving training efficiency and generalization.\n\nQuestions for further research:\n- Can the proposed analysis be extended to other optimization algorithms, such as Adam or RMSprop? How do these algorithms compare to SGD in terms of exploration behavior?\n- Are there any techniques that can be employed to encourage more exploration during training, especially in the case of larger batch sizes? How can these techniques be practically implemented?\n- How do the findings of this study align with recent advancements in optimization algorithms for deep learning, such as adaptive learning rate methods or second-order optimization techniques?\n\nIn conclusion, \"Exploring the Optimization Trajectory of Deep Neural Networks\" provides valuable insights into the behavior of optimization algorithms in deep learning. The empirical analysis and findings contribute to the understanding of SGD exploration behavior in the non-convex setting of deep neural networks and have practical implications for optimizing the training process. Further research can expand on these findings and explore additional techniques to enhance exploration in deep learning optimization.", "prompt": "You are an AI visual assistant. Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.Document is as follows. {} Instruction: {} The suggested output length is around 275 words. Output: ", "evaluation": "rouge"}
